Search Results (213)

In light of the increasing frequency and intensity of natural phenomena, whether climatic or telluric, the relevance of multi-risk assessment approaches has become an important issue for understanding and estimating the impacts of disasters on complex socioeconomic systems. Two aspects contribute to the worsening of this situation. First, climate change has heightened the incidence and, in conjunction, the seriousness of geohazards that often occur with each other. Second, the complexity of these impacts on societies is drastically exacerbated by the interconnections between urban areas, industrial sites, power or water networks, and vulnerable ecosystems. In front of the recent research on this problem, and the necessity to figure out the best scientific positioning to address it, we propose, through this review analysis, to revisit existing literature on multi-risk assessment methodologies. By this means, we emphasize the new recent research frameworks able to produce determinant advances. Our selection corpus identifies pertinent scientific publications from various sources, including personal bibliographic databases, but also OpenAlex outputs and Web of Science contents. We evaluated these works from different criteria and key findings, using indicators inspired by the PRISMA bibliometric method. Through this comprehensive analysis of recent advances in multi-risk assessment approaches, we highlight main issues that the scientific community should address in the coming years, we identify the different kinds of geohazards concerned, the way to integrate them in a multi-risk approach, and the characteristics of the presented case studies. The results underscore the urgency of developing robust, adaptable methodologies, effectively able to capture the complexities of multi-risk scenarios. This challenge should be at the basis of the keys and solutions contributing to more resilient socioeconomic systems. Full article

Urban subsurface pipeline bursts can induce catastrophic cascading effects, including ground collapse, infrastructure failure, and socioeconomic losses. However, stratum responses during the erosion cavity expansion phase and corresponding disaster mitigation strategies have rarely been researched. In this study, a numerical model validated through experimental tests was employed to investigate the effects of internal water pressures, burial depths, and different geotextile-based disaster mitigation strategies. It was revealed that a burial depth-dependent critical internal water pressure governed the erosion cavity expansion, and a predictive equation was derived based on the limit equilibrium theory. Higher internal water pressure accelerated the erosion cavity expansion and amplified the stratum stress within a range of twice the diameter D. Increased burial depth $d$ reduced peak ground heave but linearly expanded the heave zone range, concurrently elevating the overall stratum stress level and generating larger stress reduction zones (i.e., when $d/D$ = 3.0, the range of the stress reduction zone was 8.0D). All geotextile layout configurations exhibited different disaster mitigation effects (the peak ground heave was reduced by at least 15%). The semi-circular closely fitted configuration (SCCF) optimally restricted the expansion of the erosion cavity, reduced the stratum displacement (i.e., 39% reduction in the peak ground heave), and avoided stress concentration. Comprehensive analysis indicated that SCCF was suited for low-pressure pipelines in deformation-sensitive stratum and semi-circular configuration (SC) was suitable for deformation-insensitive pipeline sections. These findings provide actionable insights for tailoring mitigation strategies to specific operational risks. Full article

Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies. Full article

Losses from floods and the wide range of impacts have been at the forefront of hazard-triggered disasters in China. Affected by large-scale human activities and the environmental evolution, China’s defense flood situation is undergoing significant changes. This paper constructs a comprehensive flood disaster risk assessment model through systematic analysis of four key factors—hazard (H), exposure (E), susceptibility/sensitivity (S), and disaster prevention capabilities (C)—and establishes an evaluation index system. Using the Analytic Hierarchy Process (AHP), we determined indicator weights and quantified flood risk via the following formula R = H × E × V × C. After we applied this model to 16 towns in coastal Zhejiang Province, the results reveal three distinct risk tiers: low (R < 0.04), medium (0.04 ≤ R ≤ 0.1), and high (R > 0.1). High-risk areas (e.g., Longxi and Shitang towns) are primarily constrained by natural hazards and socioeconomic vulnerability, while low-risk towns benefit from a robust disaster mitigation capacity. Risk typology analysis further classifies towns into natural, social–structural, capacity-driven, or mixed profiles, providing granular insights for targeted flood management. The spatial risk distribution offers a scientific basis for optimizing flood control planning and resource allocation in the district. Full article

For a long time, drought disasters have brought about a wide range of negative impacts on human socio-economics. Especially in large basins with many tributaries, once hydrological drought occurs synchronously in several tributaries, the hydrological drought condition in the mainstream will be aggravated, which will lead to more serious losses. However, there is still a lack of research on the probabilistic risk of simultaneous hydrologic droughts in various areas of large watersheds. In this study, the Standardized Runoff Index was used to characterize hydrological drought, and the Standardized Runoff Index (SRI) sequence characteristics of each region were analyzed. Subsequently, a multiregional hazard encounter probability distribution model with an R-vine structure was constructed with the help of the vine copula function to study the risk pattern of simultaneous hydrological drought in multiple tributaries under environmental changes. The model results showed that the probability of the four basins gradually decreased from 7.5% to 0.16% when the SRI changed from ≤−0.5 to ≤−2.0, indicating that the likelihood of the joint distribution of the compound disaster decreases with increase in the drought extremes. Meanwhile, the probability of hydrological drought in the three major basins showed significant spatial differences, and the risk ranking was Dongting Lake Basin > Poyang Lake Basin > Han River Basin. The model constructed in this study reveals the disaster risk law, provides theoretical support for the measurement of hydrological drought risk in multiple regions at the same time, and is of great significance for the prediction of compound drought disaster risk. Full article

The October 2024 cut-off low event triggered one of the most catastrophic floods recorded in the Valencia Metropolitan Area, exposing significant vulnerabilities in urban planning, infrastructure resilience, and emergency preparedness. This study presents a novel comprehensive assessment of the event, using a multi-sensor satellite approach combined with socio-economic and infrastructure data at the metropolitan scale. It provides a comprehensive spatial assessment of the flood’s impacts by integrating of radar Sentinel-1 and optical Sentinel-2 and Landsat 8 imagery with datasets including population density, land use, and critical infrastructure layers. Approximately 199 km² were inundated, directly affecting over 90,000 residents and compromising vital infrastructure such as hospitals, schools, transportation corridors, and agricultural lands. Results highlight the exposure of peri-urban zones and agricultural areas, reflecting the socio-economic risks associated with the rapid urban expansion into flood-prone plains. The applied methodology demonstrates the essential role of multi-sensor remote sensing in accurately delineating flood extents and assessing socio-economic impacts. This approach constitutes a transferable framework for enhancing disaster risk management strategies in other Mediterranean urban regions. As extreme hydrometeorological events become more frequent under changing climatic conditions, the findings underscore the urgent need for integrating remote sensing technologies, early warning systems, and nature-based solutions into regional governance to strengthen resilience, reduce vulnerabilities, and mitigate future flood risks. Full article

Urban flooding poses escalating threats to socioeconomic stability and human safety, exacerbated by urbanization and climate change. While urban flood resilience (UFR) has emerged as a critical framework for flood risk management, existing studies often overlook the systemic integration of post-disaster recovery capacity and multidimensional interactions in UFR assessment. This study develops a novel hazard–vulnerability–exposure–defense capacity–recovery capacity (HVEDR) framework to address research gaps. We employ a hybrid game theory combined weight method (GTCWM)-TOPSIS approach to evaluate UFR in China’s Taihu Lake Basin (TLB), a region highly vulnerable to monsoon- and typhoon-driven floods. Spanning 1999–2020, the analysis reveals three key insights: (1) weight allocation via GTCWM identifies defense capacity (0.224) and hazard (0.224) as dominant dimensions, with drainage pipeline density (0.091), flood-season precipitation (0.087), and medical capacity (0.085) ranking as the top three weighted indicators; (2) temporal trends show an overall upward trajectory in UFR, interrupted by a sharp decline in 2011 due to extreme hazard events, with Shanghai and Hangzhou exhibiting the highest UFR levels, contrasting Zhenjiang’s persistently low UFR; (3) spatial patterns reveal stronger UFR in southern and eastern areas and weaker resilience in northern and western regions. The proposed HVEDR framework and findings provide valuable insights for UFR assessments in other flood-prone basins and regions globally. Full article

Flood disasters are growing more common and severe as a result of global warming and climate change. These factors intensify weather extremes, resulting in more unpredictable and disastrous floods around the world. Effective flood risk assessment is critical for reducing the socioeconomic and environmental consequences of catastrophic events. This work proposes a novel technique for flood risk assessment that combines Event Tree Analysis with Dempster–Shafer evidence theory and an optimization approach. The methodology assesses flood scenarios, as well as probabilities and outcomes, to predict risk pathways and uncertainties. Prevention measures, such as flood defenses, early warning systems, and sustainable land use practices, are evaluated for cost-effectiveness and their contribution to flood resilience. The findings emphasize the relevance of multi-layered mitigation techniques for lowering flood risks and increasing community resilience. The model presented in this paper is modular, and since it depends on expert judgement, it can be used in other geographical or regional settings with adjustments from local data and local expert assessments. Full article

With climate warming, the global precipitation patterns have undergone significant changes, which will profoundly impact flood–drought disaster regimes and socioeconomic development in key regions of human activity worldwide. The convergence zone of the Indian Ocean monsoon and Pacific monsoon in China covers most of the middle and lower reaches of the Yangtze River (MLRYR), which is located in the transitional area of the second and third steps of China’s terrain. Changes in precipitation patterns in this region will significantly impact flood and drought control in the MLRYR, as well as the socioeconomic development of the MLRYR Economic Belt. In this study, Huaihua area in China was selected as the study area to study the characteristics of regional precipitation change, and to analyze the evolution in the trends in annual precipitation, extreme precipitation events, and their spatiotemporal distribution, so as to provide a reference for the study of precipitation change patterns in the intersection zone. This study utilizes precipitation data from meteorological stations and the China Meteorological Forcing Dataset (CMFD) reanalysis data for the period 1979–2023 in Huaihua region. The spatiotemporal variation in precipitation in the study area was analyzed by using linear regression, the Mann–Kendall trend test, the moving average method, the Mann–Kendall–Sneyers test, wavelet analysis, and R/S analysis. The results demonstrate the following: (1) The annual precipitation in the study area is on the rise as a whole, the climate tendency rate is 9 mm/10 a, and the precipitation fluctuates greatly, showing an alternating change of “dry–wet–dry–wet”. (2) Wavelet analysis reveals that there are 28-year, 9-year, and 4-year main cycles in annual precipitation, and the precipitation patterns at different timescales are different. (3) The results of R/S analysis show that the future precipitation trend will continue to increase, with a strong long-term memory. (4) Extreme precipitation events generally show an upward trend, indicating that their intensity and frequency have increased. (5) Spatial distribution analysis shows that the precipitation in the study area is mainly concentrated in the northeast and south of Jingzhou and Tongdao, and the precipitation level in the west is lower. The comprehensive analysis shows that the annual precipitation in the study area is on the rise and has a certain periodic precipitation law. The spatial distribution is greatly affected by other factors and the distribution is uneven. Extreme precipitation events show an increasing trend, which may lead to increased flood risk in the region and downstream areas. In the future, it is necessary to strengthen countermeasures to reduce the impact of changes in precipitation patterns on local and downstream economic and social activities. Full article

The northern foothills of the Qinling Mountains, a critical ecological barrier and urban–rural transition zone in China, face intensifying rainstorm–flood disasters under climate extremes and rapid urbanization. This study pioneers a remote sensing-driven, dynamically coupled framework by integrating multi-source satellite data, system resilience theory, and spatial modeling to develop a novel “risk identification–resilience assessment–scenario simulation” chain. This framework quantitatively evaluates the nonlinear response mechanisms of town–village systems to flood disasters, emphasizing the synergistic effects of spatial scale, morphology, and functional organization. The proposed framework uniquely integrates three innovative modules: (1) a hybrid risk identification engine combining normalized difference vegetation index (NDVI) temporal anomaly detection and spatiotemporal hotspot analysis; (2) a morpho-functional resilience quantification model featuring a newly developed spatial morphological resilience index (SMRI) that synergizes landscape compactness, land-use diversity, and ecological connectivity through the entropy-weighted analytic hierarchy process (AHP); and (3) a dynamic scenario simulator embedding rainfall projections into a coupled hydrodynamic model. Key advancements over existing methods include the multi-temporal SMRI and the introduction of a nonlinear threshold response function to quantify “safe-fail” adaptation capacities. Scenario simulations reveal a reduction in flood losses under ecological priority strategies, outperforming conventional engineering-based solutions by resilience gain. The proposed zoning strategy prioritizing ecological restoration, infrastructure hardening, and community-based resilience units provides a scalable framework for disaster-adaptive spatial planning, underpinned by remote sensing-driven dynamic risk mapping. This work advances the application of satellite-aided geospatial analytics in balancing ecological security and socioeconomic resilience across complex terrains. Full article

Climate change poses substantial threats to natural ecosystems and human livelihoods, particularly in coastal regions, by intensifying coastal erosion. This process leads to land loss, infrastructure damage, and habitat destruction while amplifying challenges such as sea-level rise, flooding, desertification, and salinization. In Vietnam’s Red River Delta (RRD), the dynamic interplay between erosion and accretion presents a highly complex challenge, necessitating effective risk assessment and management to safeguard communities and resources. Using the principles of natural disaster risk assessment and comprehensive analysis, this study develops a coastal erosion risk assessment framework incorporating hazard, exposure, and vulnerability dimensions. The framework integrates 17 indicators, including human activities, socioeconomic factors, shoreline type, and vegetation cover, with indicator weights determined through expert evaluation and the analytic hierarchy process. The application of this framework reveals that coastal erosion risk in the RRD is relatively high, with greater risk concentrated in the central and northern segments of the coastline compared to the flanking areas. This framework offers valuable insights for coastal erosion prevention, mitigation strategies, and the optimization of coastal spatial planning. The application of coastal erosion risk assessment methods provides a relatively complete foundation for developing comprehensive prevention and adaptation solutions in the future. Through the system of parameters and corresponding weights, it provides an overview of potential responses to future impacts while identifying current high-risk zones specifically and accurately, thereby assessing the importance of each parameter on that impact. Based on specific analysis of assessment results, a reasonable resource use and management policy can be developed to minimize related natural disasters. Therefore, two main groups of solutions proposed under the “Protection—Adaptation” strategy are proposed to prevent natural disasters, minimize risks and sustainably develop the coastal area of the RRD. Full article

This study investigates and forecasts multidimensional security perceptions among Serbian university students, who are a particularly engaged and vulnerable demographic in transitional societies. It examines how demographic traits, socio-economic status, and levels of institutional trust and engagement shape students’ evaluations of security in everyday life. The study examines six primary dimensions of security perception: personal safety, safety at public events and demonstrations, perceived national threats, digital security and privacy, perception of emergencies and crises, and trust in institutions and security policies. A structured online survey was administered to a sample of 406 university students selected through non-probability purposive sampling from major academic centres in Serbia, including Belgrade, Niš, Novi Sad, and Kragujevac. The questionnaire, based on a five-point Likert scale, was designed to measure levels of agreement across the six dimensions. Data were analysed using multiple regression, one-way ANOVA, Pearson’s correlation, and independent samples t-tests. All necessary statistical assumptions were met, ensuring the reliability and validity of the results. Descriptive statistics indicated moderate to moderately high overall perceived safety, with personal safety scoring the highest, followed by digital security and disaster preparedness. Lower scores were recorded for public event safety, perceived national threats, and, in particular, trust in institutional security policies. Regression analysis revealed that key predictors of perceived safety varied across dimensions: gender was a significant predictor of personal safety. At the same time, family financial status had a strong influence on perceptions of safety at public events. These findings offer valuable insights for designing targeted risk communication, inclusive policy initiatives, and institutional reforms that aim to enhance youth resilience, civic trust, and participatory security governance, ultimately contributing to long-term social sustainability. Full article

Extreme rainfall events directly increase flood risks and further trigger environmental geological hazards (i.e., landslides and debris flows). Meanwhile, rainfall-induced risks are determined by climate and geographical factors and spatial socioeconomic factors (e.g., population density and gross domestic product). However, the middle stream of Yellow River Basin, where geological hazards frequently occur, lacks systematic analyses of rainfall-induced risks. In this study, we propose a comprehensive quantification framework and apply it to the Loess Plateau of northern China based on 40 years of climate data, streamflow measurements, and multiple spatial and geographical attribute datasets. A deep learning algorithm of long short-term memory (LSTM) was used to predict runoff, and the analytic hierarchy index was utilized to evaluate the comprehensive spatial risk considering natural and socioeconomic factors. Despite a decrease in annual precipitation in our study area of 1.46 mm per year, the intensity of heavy rainfall has increased since the 1980s, characterized by increases in rainstorm intensity (+4.68%), rainfall intensity (+7.07%), and rainfall amount (+5.34%). A comprehensive risk assessment indicated that high-risk areas accounted for 20.30% of the total area, with rainfall, geographical factors, and socioeconomic variables accounting for 53.90%, 29.72%, and 16.38% of risk areas, respectively. Rainfall was the dominant factor that determined the risk, and geographical and socioeconomic properties characterized the vulnerability and resilience of disasters. Our study provided an evaluation framework for multi-hazard risk assessment and insights for the development of disaster prevention and reduction policies. Full article

Coastal zones represent the most active interfaces where natural processes and human activities converge, making them crucial for biodiversity and socioeconomic development. These zones are characterized by their fragility and susceptibility to frequent natural disasters, such as floods and erosion, which are exacerbated by high-intensity human activities and urban expansion. The ongoing challenges posed by rising sea levels and climate change necessitate robust scientific assessments of coastal vulnerability to ensure effective disaster prevention and environmental protection. This paper introduces a comprehensive evaluation system for assessing coastal zone vulnerability, utilizing multi-source data to address ecological vulnerabilities stemming from sea-level rise and climate change impacts. This system is applied to examine the specific case of Qatar, where rapid urban development and a high population density in coastal areas heighten the risk of flooding and inundation. Employing remote sensing data and Geographic Information Systems (GISs), this research leverages spatial interpolation techniques and high-resolution digital elevation models (DEMs) to identify and evaluate high-risk zones susceptible to sea-level rise. In this study, the hydrological connectivity model, bathtub technique, and CVI are interconnected tools that complement each other to assess future flooding risks under various climate change projections, highlighting the increased probability of coastal hazards. The findings underscore the urgent need for adaptive planning and regulatory frameworks to mitigate these risks, providing technical support for the sustainable development of coastal communities globally and in Qatar. This approach not only informs policy makers, but also aids in the strategic planning required to foster resilient coastal infrastructure capable of withstanding both current and future environmental challenges. Full article

The transition from open pit to underground mining intensifies water-related hazards such as Acid Mine Drainage (AMD), groundwater contamination, and aquifer depletion, threatening ecological and socio-economic sustainability. This study develops an Inclusive Disaster Risk Reduction (IDRR) framework using a Multi-Dimensional Risk (MDR) approach to holistically assess water hazards in China’s mining regions, integrating environmental, social, governance, economic, technical, community-based, and technological dimensions. A Multi-Criteria Decision-Making (MCDM) model combining the Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluates risks, enhanced by a Z-number Fuzzy Delphi AHP (ZFDAHP) spatiotemporal model to dynamically weight hazards across temporal (short-, medium-, long-term) and spatial (local to global) scales. Applied to the Sijiaying Iron Mine, AMD (78% severity) and groundwater depletion (72% severity) emerge as dominant hazards exacerbated by climate change impacts (36.3% dynamic weight). Real-time IoT monitoring systems and AI-driven predictive models demonstrate efficacy in mitigating contamination, while gender-inclusive governance and community-led aquifer protection address socio-environmental gaps. The study underscores the misalignment between static regulations and dynamic spatiotemporal risks, advocating for Lifecycle Assessments (LCAs) and transboundary water agreements. Policy recommendations prioritize IoT adoption, carbon–water nexus incentives, and Indigenous knowledge integration to align mining transitions with Sustainable Development Goals (SDGs) 6 (Clean Water), 13 (Climate Action), and 14 (Life Below Water). This research advances a holistic strategy to harmonize mineral extraction with water security, offering scalable solutions for global mining regions facing similar ecological and governance challenges. Full article