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Business-to-business suppliers invest in safety training programs believing that such programs mitigate safety hazards, prevent workplace injuries, and create value for their customers. However, causal evidence of these effects is sparse. Study 1 uses site-level monthly data from a global oil field services company. Exploiting sharp discontinuities in safety training hours due to catastrophic accidents, the authors find that a 10% increase in safety training hours per capita decreases safety hazards per capita by 6.45%–9.57%. Study 2 measures the causal impact of business establishments' safety training intensity on their workplace injuries: it leverages Local Law 196 requiring workers at construction establishments in New York City to complete at least ten hours of safety training. This legislation reduced injury rates at construction establishments in New York City by.54–.68 percentage points (a 15.56%–18.84% decrease) relative to their counterparts. Study 3, a stated-choice conjoint experiment of business-to-business procurement professionals, documents that the focal supplier's investment in safety training increases the probability of its proposal being selected by those professionals. Collectively, these findings validate the need for suppliers to invest in safety training as a risk-mitigation vehicle that has positive implications for business-to-business buying decisions. [ABSTRACT FROM AUTHOR]

This study, which is based on actual events, presents a dynamic analysis of the development, implementation, and post-implementation review of establishing an enterprise risk management (ERM) system for a U.S. multinational nonprofit firm over a 5-year period, 2015–2020. Using the Risk and Insurance Management Society Risk Maturity Model (RIMS RMM), questionnaire-based risk data and multi-dimensional risk mapping indices are used to identify and prioritize the firm's key strategic risks leading to the development of mitigation strategies whose performance are reviewed post ERM implementation, including during the 2020 Coronavirus pandemic. The results reveal that the firm's risk management system has been ad hoc and uncoordinated. Post-implementation review of the ERM program shows improvements in financial and operating positions resulting from risk diversification, enhanced profitability, exploitation of natural hedges, and improved board governance. The results further show that the recommended mitigation strategies have been effective in managing the adverse impact of the pandemic. Overall, the evidence offered in this study provides further support on the valuation benefits of ERM maturity within a real-world environment. [ABSTRACT FROM AUTHOR]

The Indian subcontinent is highly susceptible to natural disasters. To abate the effect of the disasters, the government emanates institutional mechanisms and strategies for disaster preparedness, mitigation and response. Although there is well-established institutional framework in India for disaster management, there is no study that proposes a disaster supply chain structure under the institutional framework and integrates information and digital technologies (IDT) of industry 4.0 within it. The present study overcomes this research gap. It introduces an IDT structure within the institutional framework of India for multi-agency information sharing, coordination and decision making. It formulates a mathematical model and analyses the impact of IDT on the total response time through three scenarios. The results of scenario analysis reveal the significance of IDT on the disaster supply chains and its capability to handle information delays and IDT failure. The present study can be adopted by disaster management institutions to construct and implement better response practices at the operational, planning and strategic levels. Furthermore, the proposed model in the study can be used for vaccination planning, which includes vaccine distribution, monitoring, regulation and effective implmentation as a response against the current pandemic situation caused due to SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Cyberattacks have been increasing in volume and intensity, necessitating proactive measures. Cybersecurity risk management frameworks are deployed to provide actionable intelligence to mitigate potential threats by analyzing the available cybersecurity data. Existing frameworks, such as MITRE ATT&CK, provide timely mitigation strategies against attacker capabilities yet do not account for hacker data when developing cyber threat intelligence. Therefore, we developed a novel information technology artifact, ATT&CK-Link, which incorporates a novel transformer and multi-teacher knowledge distillation design, to link hacker threats to this broadly used framework. Here, we illustrated how hospital systems can use this framework to proactively protect their cyberinfrastructure against hacker threats. Our ATT&CK-Link framework has practical implications for cybersecurity professionals, who can implement our framework to generate strategic, operational, and tactical cyber threat intelligence. ATT&CK-Link also contributes to the information systems knowledge base by providing design principles to pursue targeted cybersecurity analytics, risk management, and broader text analytics research through simultaneous multi-modal (e.g., text and code) distillation and classification. [ABSTRACT FROM AUTHOR]

Open-type check dams are effective in mitigating sediment-related disasters in mountain regions. Unlike closed-type dams, they do not interrupt sediment transport or ecological continuity. This work reports on flume experiments that explored sediment trapping by truss and beam check dams, focusing on the impact of the relative spacing between the trusses or beams on sediment capture. We assessed the ability of adjustable check dams to preserve a dynamic sediment equilibrium by modifying the truss or beam spacing. Experiments with various gravel sizes and truss or beam spacings revealed that a smaller spacing results in a significant reduction in the proportion of sediment passing through the dam, whereas a larger spacing facilitates sediment transport through the dam. The two dam types showed different behaviors. Beams were more effective than trusses in trapping sediment, particularly at relative spacings of between 1.5 and 2.3, with the difference in sediment retention between the two types of dam reaching 43%. A practical case study of an adjustable beam check dam on Dongzhuang Stream in southern Taiwan illustrated the applicability of our research, where sediment management was notably enhanced following adjustments to the dam structure. The flexible operation of open-type, adjustable beam check dams presents a promising approach for sustainable sediment control in fluvial systems, enabling a balance between disaster mitigation and ecological conservation. [ABSTRACT FROM AUTHOR]

For earthquake mitigation and disaster management, it is important to rapidly predict modified Mercalli intensity (MMI) attenuation patterns across the entire earthquake-affected area. The quick prediction of hazardous areas after a destructive earthquake can effectively reduce the number of casualties and extent of property damage. Peak ground acceleration (PGA) is closely related to earthquake MMI. Traditional PGA predictions are constructed based on empirical formulas, which struggle to explain the nonlinear relation between PGA and epicentral distance. To address this issue, we propose a machine learning-based PGA attenuation model. We utilized seismic data from the Japan K-NET and KiK-net networks collected during 2000–2023 to construct a dataset and applied three different models to fit the data. The best-fit model was selected based on the performance results of these models. Using this model, three earthquake events with MJMA >6 in 2023 were predicted to evaluate potentially hazardous areas resulting from earthquakes. The results indicated that the model could effectively predict potentially hazardous areas and rapidly generate seismic MMI maps after an earthquake. Additionally, the machine learning model successfully addressed the nonlinear relation between PGA and epicentral distance. [ABSTRACT FROM AUTHOR]

Sustainable development goals require evaluating vulnerabilities and examining natural and climatic hazards for effective planning that reduces their impact on economic, social, and developmental efforts. Key hazards like floods, landslides, wildfires, and droughts have significantly affected terrestrial ecosystems and human societies, emphasizing the importance of comprehending these hazards. This study aimed to predict and spatially map multi-hazard, identifying historical and potential risks to inform sustainable development and construction programs that mitigate risks and promote resilience. A 34-year drought magnitude map was generated using long-term data, and ensemble and individual machine learning techniques were used to produce maps of flood, landslide, and wildfire hazards in a northwest region of Iran. Results demonstrated that ensemble learning models outperformed individual models, with the top-performing models being the weighted average (WA) of the two best models, random forest, extreme gradient boosting, WA models with over 80% accuracy, and WA incorporating all models, respectively. The CART model performed best among individual models. Variable importance analysis revealed that slope and precipitation were crucial factors for identifying high-hazard landslide areas, distance from waterways, vegetation cover, and topographic humidity index emerged as the most crucial factor for identifying flood hazard areas, while vegetation, rainfall, and proximity to roads significantly impacted wildfire hazard. The multi-hazard map produced by our study indicated that about 30% of the study area was highly and very highly susceptible to floods, landslides, wildfires, and droughts and the hazards mitigation efforts should be primarily directed to these specific portions of the study area. Our study underscored the importance of integrating long-term data and machine learning techniques in multi-hazard prediction and mapping, ultimately guiding mitigation efforts and promoting resilience in the face of natural and climatic hazards. [ABSTRACT FROM AUTHOR]

An accurate computation of the fundamental period of soil deposits, which is a significant part of dynamic characteristics, leads to the earthquake resistant design of structures and, therefore, seismic hazard mitigation. For this purpose, in this paper, an attempt has been made to present a rigorous solution for the fundamental period of three-layered linear elastic soil underlain by a bedrock subjected to SH wave propagation by utilizing the amplification function definition. The prediction accuracy of the presented solution is verified by comparing it with the numerical solution obtained with SHAKE program and the values obtained from earthquake data recorded in the specific number of instrumented geotechnical downhole arrays previously identified in the literature. The results are in close agreement with these values, which stems from the capability of the solution to take not only the values of thickness and shear wave velocity of layers but also the position of them. Apart from that, six approximate methods are employed for the sake of evaluation and comparison. Moreover, significantly, for different arrangements of three-layer soil deposits, normalized practical graphs that display the effect of salient model parameters on the fundamental period and simplify the solving process are provided. [ABSTRACT FROM AUTHOR]

The recent surge in the frequency, severity, and extent of wildfires, along with the increased risk of wildfire-induced flooding, highlights the need to quantify the potential impacts of wildfires on peak flood flows. However, supporting wildfire impact assessments with imprecise models can be challenging due to the detailed information typically required about the severity and extent of wildfires, degree of dynamic forest recovery, and a lack of postburn flow data. Moreover, making reasonable assumptions about wildfire impacts becomes difficult. To address this challenge, we propose a novel methodology for screening wildfire impact scenarios on peak flood flows in regions with limited data before a wildfire has occurred. This methodology includes prefire process-based hydrological modeling, sequentially screening short wildfire impacts, and flood frequency analysis. As a proof of concept, the current strategy has been applied to four fire-prone watersheds in Canada. Unburned and worst-burn scenarios were generated and compared to quantify changes in peak flood flows and flood frequency curves. The results indicated that annual peak flows and flood frequency curves experienced an increase in the short-term worst-burn scenario across all four watersheds. The proposed screening methodology estimates the upper limits of postfire peak flood flows, offering insights into which watersheds may be disproportionately impacted by a wildfire regime. This model outputs can be seamlessly integrated into a risk management framework to inform wildfire management decisions aimed at hazard prevention and risk reduction. Practical Applications: This study introduces a groundbreaking methodology for screening the potential impact of wildfires on peak flood flows, even in regions with limited data and before a fire occurs. By using prefire hydrological models, simulating short-term wildfire effects, and analyzing flood frequency, this approach allows for early identification of watersheds that are highly vulnerable to postfire flooding. Specifically, it distinguishes between watersheds that are strongly or weakly affected by wildfire in the worst-case scenario, where little information is available about the extent or severity of the burn. This methodology may eventually be enhanced with additional data on burn severity for specific forest types, yet it currently provides a critical tool for categorizing watershed vulnerability to wildfire-related flooding. [ABSTRACT FROM AUTHOR]

The rubble mound (RM) breakwater, which is a prevalent coastal structure worldwide, often faces the significant challenge of tsunami-induced damage. Coastal regions which are characterized by high population density necessitate robust breakwaters to withstand the destructive forces of tsunamis. The most devastating natural hazard that a breakwater could encounter during its lifespan is the tsunami. Past occurrences have revealed vulnerabilities in conventional RM breakwaters leading to failures attributed to the scouring of rubble and seabed caused by excessive seepage during tsunami overflow events. This study presents novel countermeasures aimed at mitigating the potential failure mechanisms induced by tsunamis on RM breakwaters. The proposed countermeasure elements include gabions, crown walls equipped with shear keys, and sheet piles. To assess the efficacy of these innovations, a series of tsunami overflow tests was conducted on small-scale models. The results demonstrated a marked improvement in the stability and resilience of RM breakwaters against tsunamis with the incorporation of these countermeasures. Additionally, numerical simulations were performed to determine the precise mechanisms influencing the behavior of the breakwater during tsunamis. Practical Applications: This study introduces innovative techniques for enhancing the resilience of rubble mound breakwaters against tsunamis, which have significant implications for coastal protection and disaster mitigation efforts worldwide. By incorporating gabion-based countermeasures such as wire cages filled with rocks (gabions), crown walls with shear keys, and sheet piles, substantial improvements were observed in the stability and durability of rubble mound breakwaters during tsunami events. The proposed technique enabled the rubble mound breakwater to withstand Level 1 tsunamis without undergoing any deformations. For coastal communities facing the threat of tsunamis, implementing the proposed novel gabion-based techniques offers tangible benefits in terms of enhanced protection against catastrophic tsunamis. The proposed technique provides cost-effective and adaptable solutions to existing and newly constructed rubble mound breakwaters. Through practical application of the proposed technique, coastal engineers, policymakers, and disaster management agencies can effectively mitigate the risks posed by tsunamis. [ABSTRACT FROM AUTHOR]

The presence of carbapenem-resistant Acinetobacter baumannii (CR Ab) has become one of the leading causes of life-threatening, hospital-acquired infections globally, especially with a notable prevalence in intensive care units (ICUs). The cross-transmission of microorganisms between patients and the hospital setting is crucial in the development of CR Ab colonization and subsequent infections. Recent studies indicate that colonization typically precedes infection, suggesting the effectiveness and necessity of preventing CR Ab colonization as a primary method to lower infection risks. As CR Ab infections tend to draw more attention due to their severe symptoms and poor outcomes, understanding the link between colonization and infection is equally vital. To establish a foundation for prevention and control strategies against CR Ab colonization in ICUs, we present a comprehensive review of research pertaining to CR Ab in ICUs. This encompasses an analysis of the resistance mechanisms and epidemiological characteristics of CR Ab , a discussion on associated risk factors, adverse outcomes, and an evaluation of detection methods and preventive strategies. [ABSTRACT FROM AUTHOR]

This study re-examines a broad region of the Sumatran subduction zone and off-coast southern West Java, building on findings of relative quiescence and utilizing the modified probability gain (mG) concept. By comparing pre- and post-quiescence seismicity, we identify potential earthquake sources and assess associated tsunami hazards. We propose a novel combined model integrating normalized seismicity smoothing, geodetic moment rate, and mG to characterize earthquake likelihood better. This model, coupled with a robust seismicity rate model, enables a spatiotemporal earthquake potential hierarchy for refined seismic hazard assessment. Our results confirm prior quiescence findings in specific zones and identify novel potential source regions for significant future earthquakes. We estimate tsunami height, emphasizing the importance of multiple source areas and static stress loading. By examining pre- and post-event expectations, we aim to improve understanding of major earthquakes in the Sumatran Subduction Zone and inform disaster mitigation strategies. This study provides crucial insights for enhanced regional earthquake and tsunami preparedness. [ABSTRACT FROM AUTHOR]

Accurate station-level numerical weather predictions are critical for disaster prevention and mitigation, with error correction playing an essential role. However, existing correction models struggle to effectively handle the high-dimensional features and complex dependencies inherent in meteorological data. To address these challenges, this paper proposes the dual-branch residual-guided multi-view attention fusion network (DRAF-Net), a novel deep learning-based correction model. DRAF-Net introduces two key innovations: (1) a dual-branch residual structure that enhances the spatial sensitivity of deep high-dimensional features and improves output stability by connecting raw data and shallow features to deep features, respectively; and (2) a multi-view attention fusion mechanism that models spatiotemporal influences, temporal dynamics, and spatial associations, significantly improving the representation of complex dependencies. The effectiveness of DRAF-Net was validated on two real-world datasets comprising observations and predictions from Chinese meteorological stations. It achieved an average RMSE reduction of 83.44% and an average MAE reduction of 84.21% across all eight variables, significantly outperforming other methods. Moreover, extensive studies confirmed the critical contributions of each key component, while visualization results highlighted the model's ability to eliminate anomalous values and improve prediction consistency. The code will be made publicly available to support future research and development. [ABSTRACT FROM AUTHOR]

The variability of daytime–nighttime compound heat waves (CHWs) is a highly concerning issue due to severe impacts on human and natural systems. Although several studies surveyed physical processes for the CHW occurrence, its interannual variability and associated mechanisms have not been well understood. Focusing on CHWs in the Yangtze River valley (YRV, a hotspot across China), this paper indicates an emergence of enlarged interannual variability after entering into the twenty-first century, before which the interannual variability was quite small. The possible mechanism underlying the high interannual variability is further explored in terms of atmospheric and oceanic backgrounds. The results show that the atmospheric background associated with higher-than-normal CHWs over the YRV features anticyclonic circulation anomalies tilting southeastward from the north of the YRV in the upper troposphere to the western Pacific in the lower troposphere. Accordingly, the upper-tropospheric easterly and lower-tropospheric southwesterly anomalies dominate the YRV, causing anomalous subsidence and increased humidity in situ, respectively, which benefit the increase in CHWs. The tripole (positive–negative–positive) sea surface temperature (SST) anomalies in the North Atlantic (NA) and the positive SST anomalies in the Maritime Continent (MC) also play roles in increasing the YRV CHWs by influencing the above atmospheric circulations. The NA tripole SST anomalies tend to affect the upper- and midtropospheric anticyclonic anomalies through the eastward-propagating wave train across Eurasia. The warming of the MC SSTs can impact the lower-tropospheric anticyclonic anomaly over the western Pacific via local meridional circulation. The opposite situations are applicable for decreased CHWs over the YRV. Significance Statement: Daytime–nighttime compound heat waves (CHWs) refer to persistent processes with abnormally high temperatures occurring both in daytime and nighttime. Compared with heat waves (HWs) occurring only at daytime or nighttime, the CHWs exert more severe damage to natural ecosystems and human society. Thus, understanding the physical mechanisms of CHWs is urgently needed. This study examines the interannual variability of Yangtze River valley (YRV) CHWs and finds that it exhibits a pronounced enlargement after 2000. During this period, an anomalous anticyclone tilting southeastward from the north of the YRV in the upper level to the western Pacific in the lower level is a favorable atmospheric circulation background for the increase in CHWs over the YRV, and vice versa. Sea surface temperatures (SSTs) in the North Atlantic (NA) and the Maritime Continent (MC) also play important roles. The positive–negative–positive SST anomalies in the NA and the warming SSTs in the MC tend to increase the YRV CHWs, through their influences on CHW-related atmospheric circulations in the upper troposphere and lower troposphere, respectively. These findings are expected to deepen our understanding of CHW variability, which are of great importance for disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

This study presents an integrated framework that combines spatial clustering techniques and multi-source geospatial data to comprehensively assess and understand geological hazards in Hunan Province, China. The research integrates self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) to explore the relationships between environmental factors and the occurrence of various geological hazards, including landslides, slope failures, collapses, ground subsidence, and debris flows. The key findings reveal that annual average precipitation (Pre), profile curvature (Pro_cur), and slope (Slo) are the primary factors influencing the composite geological hazard index (GI) across the province. Importantly, the relationships between these key factors and GI exhibit spatial variability, as evidenced by the random intercept and slope models, highlighting the need for customized mitigation strategies. Additionally, the study demonstrates that land use patterns and stratigraphic stratum lithology significantly impact the cluster-specific relationships between the key factors and GI, emphasizing the importance of natural resource management for effective geological hazard mitigation. The proposed integrated framework provides valuable insights for policymakers and resource managers to develop spatially-aware strategies for geological hazard risk reduction and climate change adaptation. [ABSTRACT FROM AUTHOR]

Identifying deposits of modern/historical and prehistorical tsunamis in Taiwan has been successful in the past two decades and has substantially increased the extant tsunami catalogs, which have been limited in the past four centuries due to scarce and ambiguous historical accounts. In this review, the initiation of the investigation is briefly discussed, partly in response to the latest catastrophic tsunamis in the Indo-Pacific and the stimulated public concern in Taiwan. Major developments and results of the investigation include the onset of the first stage before 2010, with findings in Keelung, the eastern coast, and Lanyu Island, and the second/ongoing stage after 2013, with findings in the northern and eastern coasts and Penghu Islands. These findings contributed to validating the debated historical events, expanding the event number and time span of the tsunami catalog, and elaborating on tsunami processes, which collectively enabled the delineation of the recurrence time intervals between events. Limitations, uncertainty, further contributions, and feedback are discussed including insights into the regional western Pacific hazards of tsunamis, earthquakes, and volcanic eruptions; the principles of recognizing tsunami deposits and processes; and the propositions of future studies and hazard mitigations in Taiwan. [ABSTRACT FROM AUTHOR]

The global climate crisis severely affects coastal communities worldwide. Not only do coastal communities have to cope with the effects of the climate crisis, such as sea-level rise, increased flooding, changing weather patterns, droughts and increased salinisation, they also have to cope with other non-climate related shocks and stresses. Many studies often focus on the “climate adaptation” processes of these “vulnerable” communities in linear and cause-and-effects ways. This study approaches the concept of climate adaptation and vulnerability from a political ecology perspective in the context of coastal Taiwan – a region heavily affected by climate change. Through in-depth interviews, this study argues that households have developed various strategies to adapt not only to environmental changes (e.g. increased cold spells and flooding) but also to top-down flood hazard mitigation planning and infrastructure, changing demographics, and encroaching solar panel companies (for the “greater good of the nation”). Furthermore, decisions made in the past have a direct impact today, such as land subsidence due to the massive shift towards aquaculture several decades ago. These factors showcase how adaptation and vulnerability are relational concepts being coshaped by both structural determinants, such as political economy and power relations, and micro-level factors, such as personal aspirations and people’s life courses. [ABSTRACT FROM AUTHOR]

The hydraulic behavior of the connection between the floor failure area and the aquifer water-conductive zone is considered to be the root cause of mine water inrush disasters. Therefore, unraveling the floor failure mechanism is particularly important for safe coal mining above the high-confined aquifer. This paper estimates the depth of the baseplate failure to be 18.4–27.3 m by combining network parallel electrical methods with drilling visualization technology. The FLAC3D-based numerical model considering the strain hardening of caved rock was established with rigorous calibration and verification. The results showed that the depth of damage to the floor is 23.1 m, and the dominating floor failure mechanism is shear failure caused by the vertical stress exceeding the rock bearing capacity. Moreover, the stress recovery process of the baseplate does not alter the failure morphology of the baseplate. Based on the above research findings, the in-situ floor control technique of the working face No. 4305 is proposed and practiced in the field. Field measurements show that floor control performance is satisfactory with water inflow in the goaf being roughly stable at 50 m3/h. Our results can provide useful reference for safe mining above confined aquifer and prevention and mitigation of water-related hazards. ● A quantitative characterization method of floor fractures using MATLAB on the basis of borehole visualization technique was proposed, and it is clear that the development of floor fractures exhibit a negative logarithmic decrease as the drilling depth increase. ● The overburden load is transferred to the floor over the compaction of the caved rock in the goaf, recovering the floor stress environment without altering its failure characteristic. ● The main factors controlling the floor failure are burial depth and working face length, and the floor failure depth increases linearly with the increase of burial depth and working face length. ● The resistance-adding repair technology of the floor water-blocking zone and the resistance-adding modification technology of the aquifer water-conductive zone were proposed. Field practice show that floor control performance is satisfactory with water inflow in the goaf being roughly stable at 50 m3/h. [ABSTRACT FROM AUTHOR]

Supraglacial debris cover considerably influences sub-debris ablation patterns and the surface morphology of glaciers by modulating the land–atmosphere energy exchange. Understanding its spatial distribution and temporal variations is crucial for analyzing melting processes and managing downstream disaster mitigation efforts. In recent years, the overall slightly positive mass balance or stable state of eastern Pamir glaciers has been referred to as the "Pamir-Karakoram anomaly". It is important to note that spatial heterogeneity in glacier change has drawn widespread research attention. However, research on the spatiotemporal changes in the debris cover in this region is completely nonexistent, which has led to an inadequate understanding of debris-covered glacier variations. To address this research gap, this study employed Landsat remote sensing images within the Google Earth Engine platform, leveraging the Random Forest algorithm to classify the supraglacial debris cover. The classification algorithm integrates spectral features from Landsat images and derived indices (NDVI, NDSI, NDWI, and BAND RATIO), supplemented by auxiliary factors such as slope and aspect. By extracting the supraglacial debris cover from 1994 to 2024, this study systematically analyzed the spatiotemporal variations and investigated the underlying drivers of debris cover changes from the perspective of mass conservation. By 2024, the area of supraglacial debris in eastern Pamir reached 258.08 ± 20.65 km2, accounting for 18.5 ± 1.55% of the total glacier area. It was observed that the Kungey Mountain region demonstrated the largest debris cover rate. Between 1994 and 2024, while the total glacier area decreased by −2.57 ± 0.70%, the debris-covered areas expanded upward at a rate of +1.64 ± 0.10% yr−1. The expansion of debris cover is driven by several factors in the context of global warming. The rising temperature resulted in permafrost degradation, slope destabilization, and intensified weathering on supply slopes, thereby augmenting the debris supply. Additionally, the steep supply slope in the study area facilitates the rapid deposition of collapsed debris onto glacier surfaces, with frequent avalanche events accelerating the mobilization of rock fragments. [ABSTRACT FROM AUTHOR]

The article discusses a recent rock avalanche event that occurred on July 10, 2024, near the Patalganga Langsi Tunnel on the Badrinath Highway in the Chamoli district of Uttarakhand, India. The region is prone to natural hazards like landslides, earthquakes, floods, and cloudbursts. The rock avalanche event caused significant damage to the tunnel and blocked the highway for several hours, but fortunately, there were no casualties. The study highlights the importance of effective disaster mitigation measures in landslide-prone areas like the Indian Himalayas. [Extracted from the article]

Landslides represent a major global natural disaster, often leading to severe consequences, including substantial loss of life and property. However, research on the spatiotemporal distribution characteristics of fatal landslide events across different climate regions and their association with precipitation remains limited. In this study, we compiled a database of non-seismic fatal landslides in China from 2010 to 2022 to examine their spatiotemporal distribution and relationship with precipitation. From 2010 to 2022, China experienced a total of 710 fatal landslide events, causing 5158 fatalities. The data revealed a declining trend in both the number of fatal landslides and associated fatalities, with the number of fatal landslides demonstrating a recurring cycle of 3–4 years marked by continuous decreases within each cycle. The initial year of a new cycle witnessed a significant increase in the number of fatal landslides, suggesting a periodic occurrence, which is related to El Niño. The central subtropical humid region recorded the highest number of fatal landslide events, attributed to its highest annual precipitation. The trend in fatal landslides closely corresponded with variations in precipitation, increasing in spring and summer and decreasing in autumn and winter. The cumulative frequency distributions of fatal landslides and fatalities followed a power-law distribution, with a sharp decline observed when exceeding a certain value, indicating a deflection effect. Despite the low population density, the plateau climate region has the highest risk of life loss among all climate regions. Understanding the spatial distribution of non-seismic fatal landslides can significantly aid in formulating more effective disaster prevention and mitigation policies. [ABSTRACT FROM AUTHOR]

The source modeling of 2021 Miyagi earthquake (Mw7.0) offers a significant tool to understand the initial evaluation of M9 earthquake cycle in this region. This article seeks to simulate the 2021 Miyagi earthquake (Mw7.0) using the modified semi-empirical technique (MSET) after incorporating site effects determined using the Horizontal to Vertical spectral ratio technique. We propose the best-fitting source model of this earthquake from a spectrum of rupture model parameters using MSET. We believe that this effort is the first to use MSET to model this earthquake and will provide significant contribution for seismic hazard assessment of the Miyagi region. [ABSTRACT FROM AUTHOR]

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This study explores the application of numerical simulation in debris flow disaster early warning, using the Shiyang Gully in China as a case study. Using both the HEC-HMS and FLO-2D, the 18 June 2017 debris flow event was reconstructed to analyze the impacts of cumulative rainfall, rainfall intensity, and rainfall range on debris flow hazards. Simulation results showed that cumulative rainfall exceeding 90 mm or rainfall intensity surpassing 200 mm/8 h significantly increases debris flow depth, impact force, and affected areas, leading to severe structural damage. Expanding the rainfall range to the entire basin further amplifies disaster risks, increasing both inundation depth and exposed elements. Based on these findings, a four-tier debris flow early warning system was developed: (1) blue (IV) warning for cumulative rainfall of up to and including 20 mm or intensity of 200 mm/24 h, indicating preparation and monitoring; (2) yellow (III) warning for rainfall exceeding 20 mm but below 60 mm, requiring enhanced inspections and safety measures; (3) orange (II) warning for rainfall between 60 and 90 mm or intensity of 200 mm/12 h, necessitating immediate evacuation preparations; and (4) red (I) warning for rainfall over 90 mm or intensity of 200 mm/8 h, demanding full evacuation and emergency responses. This study demonstrates the value of numerical simulation in refining early warning systems by integrating multi-scenario analyses of rainfall parameters. The proposed system offers scientific and practical insights for enhancing debris flow disaster management, particularly in small, high-risk watersheds, providing a framework for cross-regional disaster mitigation strategies. [ABSTRACT FROM AUTHOR]

The intertidal zone, as a dynamic ecosystem at the interface of land and sea, plays a critical role in environmental protection and disaster mitigation. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with the Advanced Topographic Laser Altimeter System (ATLAS) with the ability to penetrate the water bodies, enabling its use for bathymetric measurements. However, the complex land cover types and frequent environmental changes in intertidal zones pose significant challenges for precise measurement and dynamic monitoring. In an effort to address the denoising challenges of ICESat-2 photon point cloud data in such complex environments, this study proposes an adaptive photon denoising method that is capable of dynamically adjusting the denoising strategy for different types of photon data. ATL03 data from four typical intertidal zones were selected for denoising experiments. The results indicated that the proposed adaptive denoising method achieved average recall, precision, and F-score values of 0.9885, 0.9927, and 0.9906, respectively, demonstrating excellent denoising performance and stability. This method provides an effective data processing approach for high-precision monitoring of intertidal zone topography. [ABSTRACT FROM AUTHOR]

China's Dongting Lake area is intertwined with rivers and lakes and possesses many water systems. As such, it is one of the most complicated areas in the Yangtze River Basin, in terms of the complexity of its flood control. Over time, siltation and reclamation in the lake area have greatly weakened the river discharge capacity of the lake area, and whether it can endure extreme floods remains an open question. As there is no effective scenario simulation model for the lake area, this study constructs a hydrological model for the Jingjiang–Dongting Lake system and verifies the model using data from 11 typical floods occurring from 1954 to 2020. The parameters derived from 2020 data reflect the latest hydrological relationship between the lake and the river, while meteorological data from 1954 and 1998 are used as inputs for various scenarios with the aim of evaluating the flood pressure of the lake area, using the water levels at the Chengglingji and Luoshan stations as indicators. The preliminary results demonstrate that the operation of the upstream Three Gorges Dam and flood storage areas cannot completely offset the flood pressure faced by the lake area. Therefore, the reinforcement and raising of embankments should be carried out, in order to cope with potential extreme flood events. The methodology and results of this study have reference value for policy formation, flood control, and assessment and dispatching in similar areas. [ABSTRACT FROM AUTHOR]

Landslide detection is one of the crucial tasks in geological hazard prevention and control. Accurate detection and prediction of landslide areas contribute to taking appropriate preventive and mitigation measures in advance. However, it still faces challenges in terms of timeliness and accuracy. Our design motivation is to seek a network that can demonstrate excellent performance and relatively low computational cost in practical landslide area detection tasks. The proposed method in this paper can simultaneously capture local information and global dependencies in the images while achieving outstanding performance with fewer parameters. Moreover, considering the spatial correlation of landslides in remote sensing images, we introduce graph convolution to model the internal topological relationship within areas, with superpixels serving as guidance. Extensive experiments on two publicly available landslide datasets demonstrate that our network outperforms traditional and existing state-of-the-art methods while achieving the optimal balance between efficiency and performance, thus exhibiting broad potential for practical applications. [ABSTRACT FROM AUTHOR]

In this study, quantitative precipitation forecasts (QPFs) for 24-h Mei-yu rainfall at the short range (days 1–3) during May-June of 2012–2014 by a cloud model at two different grid sizes of 2.5 and 5 km are compared using point-to-point categorical measures. With strong topographic control and enhancement, abundant Mei-yu rainfall in Taiwan allows for the use of very high thresholds up to 500 mm (per 24 h), and classification based on observations is also performed to isolate the larger 16% (group A) and the largest 4% of events (group A+) from all samples. Our results show clear improvements in threat scores in heavy rainfall, with the greatest gain (by 0.16) on day 1 at the highest threshold adopted (500 mm) in the largest events of group A+, when a finer grid is used. Improvements are seen at thresholds ≥ 200 mm on day 1, ≥ 100 mm on day 2, and over 50–350 mm on day 3, mainly due to a better capability of the finer model to simulate heavy rainfall in larger events over and near the terrain. The present work provides new insights into the importance and usefulness of increasing model resolution, when and if QPFs of heavy rainfall at precise locations are crucial for hazard mitigation. Similar benefits are not as evident in the literature, likely because the thresholds used were not high enough, the larger events were not isolated, or the impact of topography on rainfall is not as strong and apparent as in Taiwan. [ABSTRACT FROM AUTHOR]

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Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tsunamis pose serious threats to coastal regions, particularly regions with critical infrastructure. Recent events in the Indian Ocean and Japan have demonstrated the necessity of conducting comprehensive tsunami hazard analyses across regions including Oman which has experienced historical tsunamis generated from Makran Subduction Zone (MSZ). This study seeks to assess the tsunami hazard for Wudam As-Sahil coast in northern Oman using both deterministic and probabilistic approaches, focusing on earthquake-generated tsunamis from the MSZ. The research employs Deterministic Tsunami Hazard Assessment (DTHA) to model worst-case tsunami scenarios and Probabilistic Tsunami Hazard Assessment (PTHA) to estimate wave height probabilities over various exposure times. Numerical models simulate tsunami generation, propagation, and inundation based on historical and hypothetical earthquake events. The DTHA results indicate that maximum tsunami wave heights could reach 3 m. In contrast, PTHA findings suggest a low probability of waves exceeding 1 m. Furthermore, this study identified Mw 7.2 western MSZ scenario as the most hazardous scenario for Wudam As-Sahil coast with potential run-up heights reaching up to 2.7 m. The findings underscore the moderate tsunami risk facing the Wudam As-Sahil coast. The hazard assessments provide valuable insights for disaster preparedness, indicating areas in need of mitigation measures and emergency planning efforts. [ABSTRACT FROM AUTHOR]

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Large strike-slip earthquakes are generally characterized by long surface rupture zones and relatively concentrated displacement distribution. The displacements on main seismogenic faults have been well studied and assessed by numerous empirical relations. Detailed mapping of the deformation zone of strike-slip earthquakes in the past decades indicates that distributed ruptures beyond the main faults have controlled the width of surface deformation zones and influenced the distribution of damages and earthquake-induced geological disasters. Therefore, the displacement hazard assessment from distributed surface rupture along strike-slip faults is urgent for disaster prevention and mitigation and the seismic safety of linear engineering. The Tibetan Plateau is marked by a series of strike-slip faults accompanied by lateral extrusion of material due to the Cenozoic collision of the Indian and Eurasian plates. In this study, we collected the surface rupture data of five strike-slip earthquakes in the Tibetan Plateau during the past decades, including the 1997 Mani ( M W 7.5), 2010 Yushu ( M W 6.9), 2014 Yutian ( M W 6.9), 2021 Maduo ( M W 7.4), and 2022 Menyuan ( M W 6.6) earthquakes. Then, we preprocess the original data to form the standardized dataset after removing the fractures due to non-tectonic factors such as landslides, gravity instability under seismic ground motion, and so on. Based on the standardized dataset, the surface rupture displacements generated by strike-slip faults are incorporated into a probabilistic displacement hazard analysis framework, and a probability model of the surface rupture displacement distribution is established for the Tibetan Plateau. This model estimates the probability per unit area of finding a distributed rupture that allows a displacement that exceeds a displacement threshold at a given distance from the principal fault. This study not only provides a framework for the probabilistic displacement hazard of distributed ruptures from strike-slip faults but also supports the seismic hazard assessment of linear engineering crossing strike-slip faults in the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Against the backdrop of global warming and rising sea levels, storm surge disasters occur frequently, often forming complex chains of events that lead to severe crises. However, systematic research on storm surge disaster chains is scarce. To characterize these chains, this research proposes a storm surge disaster chain analysis system based on complex networks and Bayesian networks. The system consists of three modules: evaluation, prediction, and measurement. The evaluation module uses a complex network model to quantitatively analyze the vulnerability, key nodes, and critical transmission paths of the disaster chain complex network. The prediction module establishes a Bayesian network-based model to forecast the complex network evolution process, forecasting the occurrence probability and loss scenarios of the disaster events. The measurement module measures and calculates the chain effect based on the dependence relationship and loss degree of the disaster event loss scenario. The results elucidate that most key nodes are primary and secondary disasters such as seawater flooding, flooding, dam damage, rainstorm, and house damage. Meanwhile, edges such as the sea wave–seawater flooding and house damage–human casualties have a critical impact on the storm surge disaster chain complex network. Key evolutionary paths such as strong winds–human casualties and over-warning tide level–social influence need to be focused on. Disaster reduction strategies such as maintaining dams, reinforcing houses, and removing disaster-bearing body can effectively break the chain and mitigate disasters. This research has a reference value for the scientific understanding of storm surge disaster chains and can serve as a scientific basis for comprehensive disaster reduction, disaster preparedness, and disaster relief. [ABSTRACT FROM AUTHOR]

Introduction: Air pollution is a major environmental health problem affecting everyone.Few studies have revealed the specific needs of the residents; hence the thought demand for residents to the knowledge of air pollution information was explored using a questionnaire. Aims and objective: To develop awareness and understanding of pollution hazards and their prevention among the Kolar population on air pollution by using a questionnaire. Materials and methods: A cross-sectional study was conducted among 120 subjects in Kolar town by Purposive sampling technique with a validated, standard questionnaire by interview method and collected was analysed by SPSS version 20. Results: Majority gained knowledge on air pollution from mobile phones and television, While a few said from the public and newspapers. Regarding attitude, 91% agreed that air pollution affected their health and the environment, with 116 (96.7%) 106 (88.3%) saying it from outdoor and indoor activities respectively. Statistically significant association was established between knowledge gained among educators with (P=0.004) and attitude toward outdoor air pollution with (P=0.001) and changes in behavioural patterns with (P=0.042). Conclusion: This work served as a starting point for a more timely and complete survey on air pollution and its relation to health and economic growth. [ABSTRACT FROM AUTHOR]

The protection of wetlands plays a critical role in ecosystem health and human welfare. This research paper explores the role of major types of state action plans, including climate action plans (CAPs), hazard mitigation plans (HMPs), nonpoint source pollution management plans (NPSPs), long range transportation plans (LRTPs), and state wildlife action plans (SWAPs), to evaluate how well wetland conservation efforts have been integrated in these state-level plans in the 50 US states. A plan evaluation protocol with 15 indicators was developed to evaluate the plan integration across the 196 state-level action plans. The findings suggest that, even though various wetland conservation initiatives operate within the realm of environmental protection, a considerable number of these plans dedicate limited direct focus to wetland preservation. While the primary emphasis of these plans may not be explicitly on wetland conservation, the results unveil the degree of integration between wetland conservation and other state-level planning endeavors. The indicators show that the SWAPs obtained the highest grade among the five types of plans due to the closest natural connections of wildlife with wetland habitat. The LRTPs received the lowest score among all the plans, as expected, due to low scope of covering environmental issues. HMPs, NPSPs, and CAPs have considered wetland conservation as a useful mitigation tool in hazard risk reduction, water quality improvement, and climate adaptations, but large variations still exist among the different states. Additionally, among the indicators, coordination and policy development received relatively lower scores, suggesting that state agencies should work more proactively and collaboratively to improve wetland conservation. Incorporating wetland protection and restoration efforts in the state-level planning mechanisms is an important step toward achieving the national goal of no net loss of wetlands and improving the quantity and quality of wetlands in the United States. [ABSTRACT FROM AUTHOR]

Purpose: This research explores the drivers that determine the ability of spontaneous volunteer groups (SVGs) to sustain their operations. That sustainability aims to support those affected in the community beyond the response phases of a disaster and into the recovery and mitigation phases to build resilience to the next disaster. Design/methodology/approach: To investigate the sustainability of spontaneous volunteering that takes place in the aftermath of a disaster, we conducted qualitative interviews in three English locations where groups of spontaneous volunteers emerged following major floods. We analysed our qualitative data using thematic analysis. Findings: Our findings theorise the drivers of SVG sustainability and present these in four themes: (1) assessment of ongoing needs; (2) organisation of resources to address that need; (3) leadership and followership creating a weight of operational capability and (4) influence of political will. Through exploring these drivers, we uncover key factors to developing a sustainable SVG system including trusted leadership and social capital. Research limitations/implications: We show how the four drivers interact to support the continuity of SVGs and sustain their operations. This has implications for how leaders of SVGs create a volunteering environment that encourages ongoing involvement and has implications for officials to view SVGs as a support rather than a risk. Originality/value: The novelty of our paper is in rejecting the argument of the temporal limit of SVGs to the response phase by theorising the drivers that make their operations sustainable for recovery and resilience building to mitigate the next disaster. This includes our examination of the interplay between those drivers. [ABSTRACT FROM AUTHOR]

In the context of rapid urbanization, the proliferation of high-density residential zones and intricate infrastructure networks markedly amplifies a city's susceptibility to natural calamities, notably seismic events. Thus, a precise evaluation of a city's emergency capability for seismic events is imperative. This research proposes a novel and all-encompassing evaluation framework for indicators, grounded in crisis management theory, covering the entire spectrum of disaster mitigation, preparedness, response, and recovery. The framework comprises four primary dimensions and 15 auxiliary indicators, synergistically integrating quantitative and qualitative methodologies. Employing the coefficient of Coefficient of Variation Method and the Delphi Method, the study assigns weights to the indicators, while the 2-tuple fuzzy linguistic approach adeptly manages uncertain information. Utilizing Changchun City as an exemplar, the constructed and analyzed model highlights the city's strengths in emergency supply reserves and the formulation of emergency plans. However, the findings indicate a pressing need for enhancements in seismic preparedness, monitoring and early warning systems, urban economic resilience, and public education initiatives. This study not only furnishes a robust framework for evaluating disaster emergency capabilities specific to Changchun City but also imparts valuable insights applicable to seismic disaster management in other urban contexts. It substantially contributes to the theoretical and practical discourse on augmenting urban resilience in the face of natural disasters. [ABSTRACT FROM AUTHOR]

Considering the high number of wells in offshore formations such as the Gulf of Mexico having a risk of leaking gas into the surrounding formations, a deep understanding of the fate and transport of gas released from damaged wells is of special relevance for hazard assessment and prevention in offshore petroleum operations. This work explores a robust strategy to reduce the risk and impact of contaminant releases in an offshore formation by analyzing the applicability of machine learning technology as a tool to forecast the information regarding a possible broaching in a loss of containment scenario of an offshore well. Numerical simulations to describe the system behavior in hydrate-bearing media were implemented to generate the data regarding the broaching day and location, and the hydrate mass generated and the total released CH4 in gas phase in the system. Using the data generated from the different scenarios, we trained multiple Artificial Neural Networks for the prediction of the different outputs, which showed excellent correlations between the input and output features. This is the first study to combine machine learning technology for advanced reservoir simulation to reduce the broaching hazard of gas escaping from an offshore production well. [ABSTRACT FROM AUTHOR]

Weather forecasting is a classical problem in remote sensing, in which precipitation is difficult to predict accurately because of its complex physical motion. Precipitation significantly impacts human life, work, and the ecological environment. Precise precipitation forecasting is vital for water resource management, ecological protection, and disaster mitigation through precise precipitation forecasting. This study introduces an innovative deep learning-based precipitation-forecasting method DFMM-Precip that integrates reanalysis of precipitation data and satellite data using a multi-modal fusion layer and predicts future precipitation details through a global–local joint temporal-spatial attention mechanism. By effectively combining satellite infrared data with reanalysis data, the approach enhances the accuracy of precipitation forecasting. Experimental results for 24 h precipitation forecasts show that DFMM-Precip's multi-modal fusion layer successfully integrates multi-modal data related to precipitation, leading to improved forecast accuracy. In particular, the global–local joint temporal-spatial attention mechanism provides precise, detailed forecasting of spatial and temporal precipitation patterns, outperforming other state-of-the-art models. The MSE of the forecasting results is 10 times lower than that of the advanced RNN model and 2.4 times lower than that of the advanced CNN model with single-modal data input. The probability of successful rainfall prediction is improved by more than 10%. [ABSTRACT FROM AUTHOR]