Vulnerability Assessment Model Research Articles

Grey Bayesian network model for electromagnetic pulse vulnerability assessment of engine system

Firstly, in view of the uncertainty of electromagnetic pulse vulnerability assessment of engine system caused by limited experimental data and incomplete information, a grey Bayesian network model is proposed by introducing interval grey numbers in grey system theory to characterize the uncertainty of sensitivity threshold and fault logic relationship of engine system components, so as to improve the processing ability of Bayesian network for uncertain information. Secondly, taking broadband high-power microwave as an example, the interval grey number failure probability of engine system and the posterior failure probability of sensor interval grey number are calculated. The former represents the survivability of the engine system as a whole under strong electromagnetic pulse, and the latter reflects the vulnerability order of each sensor under the condition of engine system failure. The evaluation conclusion can provide a reference for the design of vehicle electromagnetic protection.

Dynamic vulnerability assessment framework for urban flood disasters considering real-time population changes

ABSTRACT Flood vulnerability assessment is an important means to cope with urban flood disasters. However, urban population mobility and land-use changes bring significant uncertainty to vulnerability assessments. In order to respond to the refined and dynamic needs of urban flood disaster prevention and control, a multi-scale and multi-categorized urban flood dynamic vulnerability assessment framework that considers real-time population changes was proposed in this study. The framework used the comprehensive function of exposure, sensitivity, and coping ability under population dynamic changes to characterize urban flood dynamic vulnerability, and constructed urban flood disaster vulnerability assessment models at both grid and regional scales. The results show that population mobility has a significant impact on the vulnerability of urban flood disasters. The vulnerability of flood disasters in Zhengzhou City is highest at 8:00 and 18:00, and high-vulnerability areas are mainly concentrated in residential areas in the central part of the city and main transportation channels. From 10:00 to 15:00, the high-vulnerability areas migrate to public service areas in the eastern part of Zhengzhou City. In the evening, the vulnerability degree of flood disasters is relatively low. The research results can provide a theoretical basis for urban flood managers to prevent and control flood disasters.

Vulnerability assessment and evolution analysis of Beijing's Urban Rail Transit Network

With the development of urban rail transit system, the complexity of the network intensifies, and its vulnerability shifts correspondingly. Understanding the characteristics and evolution of network vulnerability, as well as identifying developmental patterns, enables more scientific network planning. Current researches on network vulnerability predominantly focus on the static vulnerability assessment of existing networks, with limited studies on vulnerability evolution. This paper divides the topological evolution of the Beijing Urban Rail Transit Network (BURTN) from 2000 to 2020 into Formation and Improvement stages using the K-means++ method. By constructing a multidimensional vulnerability assessment model that considers node degree uniformity, network efficiency, and connectivity, the vulnerability evolution characteristics and patterns of BURTN are explored in cases of both Single-station failures and Multi-station consecutive failures (including random and intentional failures). Furthermore, the evolutionary relationship between network vulnerability and network structure is explored using the Ridge regression model. Calculations reveal that in the case of Single-station failures, during the Formation stage, the proportion of highly vulnerable stations (HVS) and the impact of each station failure on network performance decrease significantly, by 69.36 % and 67.67 %, respectively. During the Improvement stage, the proportion of HVS decreases significantly, while the impact of each station failure on network performance decreases slightly, by 79.10 % and 37.04 %, respectively. In the case of Multi-station consecutive failures, during the Formation stage, the network’s ability to cope with both random and intentional failures decreases, with the percentage of network nodes removed at the collapse state decreasing by 24.95 % and 11.12 %, respectively. During the Improvement stage, the network’s ability to cope with random failures remains stable, while its ability to cope with intentional failures decreases. This study helps to understand vulnerability from an evolutionary perspective and provides practical strategies for reducing vulnerability.

Vulnerability and driving mechanism of four typical grasslands in China under the coupled impacts of climate change and human activities

Understanding of how different grasslands types respond to climate change and human activities across different spatial and temporal dimensions is crucial for devising effective strategies to prevent grasslands degradation. In this study, we developed a novel vulnerability assessment model for grasslands that intricately evaluates the combined impact of climate change and human activities. We then applied this model to analyze the vulnerability and driving mechanism of four representative Chinese grasslands to climate change and human activities. Our findings indicate that the vulnerability of the four grasslands would show a pattern of higher in the west and lower in the east under the influence of climate change alone. However, when human activities are factored in, the vulnerability across the four grasslands tends to homogenize, with human activities notably reducing the vulnerability of alpine grasslands in the west and, conversely, increasing the vulnerability of grasslands in the east. Furthermore, our study reveals distinct major environmental drivers of grasslands vulnerability across different regions. The two western alpine grasslands exhibit higher vulnerability to annual mean temperature and isothermality compared to the eastern temperate grasslands, while their vulnerability to precipitation of the coldest quarter is lower than that of the eastern temperate grasslands. These findings are helpful for understanding the multifaceted causes and mechanisms of grasslands degradation, providing a scientific foundation for the sustainable management and conservation of grassland resources.

Deep learning-based vehicular engine health monitoring system utilising a hybrid convolutional neural network/bidirectional gated recurrent unit

Vehicles play a pivotal role in the current era of Industry 4.0 by providing passengers with excellent mobility, comfort, and safety while strengthening national and international economies. Unanticipated vehicular engine issues can hinder performance and lead to costly maintenance. As analytics processes become faster, more accurate, and more reliable, intelligent fault prediction and diagnosis for vehicles, particularly engines, is becoming increasingly popular. To date, hybrid deep learning approaches to vehicle engine diagnostics have been limited, and none have used engine health monitoring and categorisation based on vulnerability assessment and vehicle structural information. This paper introduces a hybrid deep learning-based vehicular engine health monitoring system (VEHMS) decision model using Deep CNN (convolutional neural network)-BiGRU (bi-directional gated recurrent unit). This model monitors a vehicle’s engine health in real-time and classifies its status as good, critical, moderate, or minor condition. Several advanced and hybrid deep learning algorithms were applied to monitor engine health and categorise its status by integrating sensor data with evaluated vulnerability information from an infrastructure vulnerability assessment model. The Deep CNN-BiGRU-based VEHMS decision model outperformed other techniques with an accuracy of 0.8897, ensuring minimal decision losses while classifying engine conditions. This study aims to contribute to developing comprehensive vehicle health monitoring systems and advance the automotive industry by incorporating more intelligent features. The proposed approach can enhance vehicle performance, reliability, and efficiency in the transportation sector by improving engine health monitoring.

Predicting vulnerability through hybrid modeling combining GAM and XGBoost - A case of affected population vulnerability to tropical cyclone in Hainan Province

Tropical cyclones are one of the most destructive natural disasters, frequently occurring in the southeastern coastal areas of China, causing huge losses. Accurate characterization and prediction of negative impacts are necessary to minimize impact on coastal cities. However, a complex interaction between hazard intensity and other drivers is involved in the impact of some extreme tropical cyclones, which are often worthy of attention. In this study, a quantitative vulnerability assessment model of tropical cyclones considering the mutual relation between hazard intensity and environmental drivers was developed using the generalized additive model (GAM), random forest (RF), and extreme gradient boosting (XGBoost) for the case of Hainan Province, China. In the testing dataset, the root mean squared error of the optimal model was 0.1393, and the mean absolute percentage error of the optimal model was 35.2 % for spikes in the affected population rate. The spikes in the affected population rate were predicted well by the hybrid model jointly considering nonlinear multivariable regression models and undersampling machine learning methods.

Towards sustainable coastal management: a hybrid model for vulnerability and risk assessment

This paper presents the development of a Hybrid Model (HM) integrated with a Bayesian Network (BN) for comprehensive coastal vulnerability and risk assessment, with a focus on Konyaaltı Beach, Antalya, Turkey. The HM incorporates critical environmental parameters such as wind, waves, currents, and sediment transport to simulate conditions at vulnerable coastal areas and perform risk assessments for storm effects, flooding, and erosion. The model includes submodules for predicting coastal storms, quantifying sediment transport rates, assessing tsunami inundation severity, and categorizing storms based on beach typologies. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized for significant wave height predictions, enhancing the model's accuracy. The integration of hydrodynamic modeling, Bayesian networks, and ANFIS offers a robust framework for assessing coastal vulnerability and informing sustainable management practices. The study's results highlight the necessity for integrated risk management strategies, including adaptive infrastructure design, zoning and land use regulations, ecosystem-based management, and continuous monitoring and model refinement to enhance coastal resilience against dynamic environmental forces. This research provides valuable insights for mitigating the impacts of hazards on urban developments, contributing to the advancement of sustainable coastal management.

Urban flood-bearing vulnerability evaluation based on the moment estimate weighting and improved gray target model.

Increasingly severe flooding seriously threatens urban safety. A scientific urban flood-bearing vulnerability assessment model is significant to improve urban risk management capacity. The gray target model (GTM) has advantages in urban flood-bearing vulnerability assessment. However, indicator correlation and single bull's-eye are commonly neglected, leading to defective evaluation results. By integrating the four base weights, an improved weighting method based on the moment estimate was proposed. Then, the marginal distance was used to quantify the indicator correlation, and the TOPSIS model was introduced to define the relative bull's-eye distance. Thus, an improved gray target evaluation method was established. Finally, an urban flood-bearing vulnerability evaluation model was presented based on the moment estimate weighting-improved GTM. In this study, Zhengzhou City, China, was taken as an example. The spatial and temporal changing characteristics of the flood-bearing vulnerability of Zhengzhou from 2006 to 2020 were investigated. The results show that: (1) On the temporal scale, the disaster-bearing vulnerability of Zhengzhou City showed an upward trend during the 15 years; (2) On the spatial scale, Guancheng District of Zhengzhou City had the relatively highest vulnerability to urban flooding. This study is expected to provide a scientific reference for urban flood risk management.

Integrated management of safety and security barriers in chemical plants to cope with emerging cyber-physical attack risks under uncertainties

Chemical facilities face threats from accidental and intentional events, including the rising concern of cyber-physical (C2P) attacks in the digitized industrial control system era. Addressing major accident risks from safety hazards and C2P attacks requires an immediate unified framework for safety and security barrier management. This study presents a systematic risk-based approach to integrate conventional safety risks with emerging C2P attack risks. Adverse scenarios are identified, integrated into an attack-tree-bow-tie diagram, and modelled using a Bayesian network (BN). A vulnerability assessment model is developed to quantify industrial control system vulnerability to C2P attacks, considering uncertainties in attackers' knowledge levels. Monte Carlo simulations are used to handle uncertainty propagation in risk assessment, allowing the use of probability distributions for BN root nodes. Sensitivity analysis identifies critical factors/events, guiding the proposal of candidate strategies for barrier improvements. Combining cost-effectiveness analysis with a risk matrix yields the optimal strategy for safety and security barrier enhancements based on risk estimations. A hypothetical case study demonstrates the proposed approach's effectiveness in integrated safety and security barrier management, considering security vulnerability patching and safety barrier maintenance scheduling from a cost-effective perspective.

Quantitative Assessment and Validation of Groundwater Pollution Risk in Southwest Karst Area

AbstractGroundwater pollution risk assessment is a useful tool for groundwater pollution prevention and control. However, it is difficult to accurately quantify groundwater flow and contaminant fluxes in karst areas and different types of karst areas have different hydrogeological characteristics. Therefore, the assessment of groundwater pollution risk in karst areas must use different assessment indicator systems. This study developed a new methodology that modified the vulnerability assessment model PLEIK, determined pollutant fluxes considering hydrogeological conditions, and revised parameter weights using the random forest method. The resulting PLEIKD-RF model was used to assess the risk of groundwater contamination in the southwestern karst region and its validity was verified. The results showed that the groundwater pollution risk in the region was low, with 65.64% of the low and relatively low risk areas located in the middle and high mountainous regions. 11.81% of the high and relatively high risk areas were sporadically located in the western and central regions, which were mainly controlled by the distribution of the pollution sources and the karst development. The accuracy of the results of groundwater pollution risk assessment in the study area was 71.87% as verified by the horizontal difference method. The results of the sensitivity analysis indicated that accurate, detailed, and representative data on the protective layer, surface water-groundwater interactions, and pollution source loads would improve the accuracy of groundwater pollution risk zoning. This assessment method provided a reference for similar assessments and the results provide a basis for the protection and management of groundwater resources in the region.

Tackling digital payment frauds: a study of consumer preparedness in India

Purpose This paper aims to present a study to measure “Consumer Preparedness” (CP) towards digital payment frauds by considering factors such as awareness regarding fraud risk (“Awareness”), implementation of measures (“Protection”) and actions to be taken in case of fraud (“Responsiveness”). Design/methodology/approach This study reviews existing literature to understand various typologies of digital payment fraud. The data of 372 consumers was collected using a structured questionnaire. The data was analyzed using analysis of variance (ANOVA) and Chi-square. CP score was calculated based on awareness score, protection score and responsiveness score. Findings The study shows that the score for the level of awareness was low, for the level of protection was moderate and for the level of responsiveness was high, leading to an overall moderate level of preparedness. Further, a moderate association was observed between demographic factors and the level of preparedness. Practical implications The authors recommend proactive and reactive measures for Central Banks regarding central fraud registry and intelligence exchange, consumer fraud vulnerability assessment model and mandating fraud risk management controls. Further, financial institutions are recommended to permit payment from registered devices only, implement strong customer authentication (including biometric authentication) and conduct periodic awareness sessions. Originality/value The existing body of knowledge does not have a model or scoring mechanism to assess the preparedness of consumers to tackle digital payment fraud. The research paper adds a classification of fraud typologies and an exploratory approach to measure the CP score.

Spatial Vulnerability Assessment for Mountain Cities Based on the GA-BP Neural Network: A Case Study in Linzhou, Henan, China

Mountain cities with complex topographies have always been highly vulnerable areas to global environmental change, prone to geological hazards, climate change, and human activities. Exploring and analyzing the vulnerability of coupling systems in mountain cities is highly important for improving regional resilience and promoting sustainable regional development. Therefore, a comprehensive framework for assessing the spatial vulnerability of mountain cities is proposed. A vulnerability assessment index system is constructed using three functional systems, ecological protection, agricultural production, and urban construction. Subsequently, the BP neural network and the genetic algorithm (GA) are combined to establish a vulnerability assessment model, and geographically weighted regression (GWR) is introduced to analyze the spatial influence of one-dimensional systems on the coupling system. Linzhou, a typical mountain city at the boundary between China’s second- and third-step terrains, was selected as a case study to demonstrate the feasibility of the framework. The results showed that the vulnerability of the ecological protection system was highly aggregated in the east–central region, that of the agricultural production system was high in the west, and that of the urban construction system was low in the central region and high in the northwestern region. The coupling system vulnerability was characterized by multispatial distribution. The complex topography and geomorphology and the resulting natural hazards are the underlying causes of the vulnerability results. The impact of ecological and urban systems on the coupling system vulnerability is more prominent. The proposed framework can serve as a reference for vulnerability assessments of other similar mountain cities with stepped topographies to support the formulation of sustainable development strategies.

Enhanced groundwater vulnerability assessment to nitrate contamination in Chongqing, Southwest China: Integrating novel explainable machine learning algorithms with DRASTIC-LU

ABSTRACT Groundwater vulnerability to nitrate assessment serves as a measure of potential groundwater nitrate pollution in a target area. This study applies the DRASTIC-LU framework, nitrate distribution data, and three machine learning models (RF, XGB, SVM) to classify nitrate levels (exceeding 10 mg/L as nitrogen) in Chongqing, China. Model evaluation uses accuracy and F1 score metrics, with RF achieving the highest accuracy (92.9%), kappa (0.857), and AUC (0.948) on test dataset. Furthermore, the SHAP interpreter revealed that aquifer conductivity, lithology, agricultural activities, areas with high-intensity development, and groundwater recharge are the most influential indicators of groundwater vulnerability. The final groundwater vulnerability level distribution map, with a resolution of 1 km × 1 km, reveals that high and extremely high vulnerability levels are concentrated in areas with high-intensity urban development and karst trough valleys in the southeastern, northeastern, and central urban areas. This work represents the first attempt of using machine learning models for groundwater vulnerability assessment in the Chongqing region. It provides theoretical support for the construction layout of groundwater monitoring stations and the prevention and control of groundwater pollution in the future.

Analysis of landscape pattern vulnerability in Dasi river basin at the optimal scale

Since the reform and opening up in 1978, the Dasi River Basin within Jinan’s startup area from replacing old growth drivers with new ones (startup area) has experienced rapid urbanization and industrialization, and the landscape pattern has changed significantly, resulting in a series of eco-environmental problems. In order to more accurately identify the vulnerable areas of landscape pattern, understand their cause mechanism and changing laws, and provide a theoretical basis for the implementation of sustainable landscape pattern planning and management in the region. Four Landsat images of 2002, 2009, 2015 and 2020 were taken as data sources, and the optimal granularity of landscape pattern analysis was determined from the perspective of landscape level and class level by using the coefficient of variation method, granularity effect curve and information loss model, and the optimal amplitude was determined by using the grid method and semi-variance function. Then, the landscape vulnerability assessment model was constructed based on the optimal scale, and its spatiotemporal evolution characteristics and spatial autocorrelation were analyzed. The result showed that: (1) The optimal granularity of landscape pattern analysis in this study area was 80 m, and the optimal amplitude was 350 × 350 m. (2) During 2002–2020, the overall vulnerability of landscape pattern in the southern part of the study area showed an increasing trend, while that in the middle and northern parts showed a decreasing trend. (3) The mean values of the vulnerability index of the overall landscape pattern in 2002, 2009, 2015 and 2020 were 0.1479, 0.1483, 0.1562 and 0.1625, respectively, showing an increasing trend year by year. In terms of land use, during 2002–2020, the average vulnerability indices of forestland and built up land increased by 23.18% and 21.43%, respectively, followed by water body and bare land, increased by 12.18% and 9.52%, respectively, while the changes of cropland and grassland were relatively small, increasing by 5.36% and 5.65%, respectively. (4) During 2002–2020, the landscape pattern vulnerability showed a significant spatial positive correlation in terms of spatial distribution. The Low-Low areas were generally transferred from the southeastern and midwestern to the middle and northern, and the High–High areas were mainly transferred from the middle to the southern. Overall, the degree of the spatial agglomeration of the landscape pattern vulnerability showed an increasing trend.

Precondition Study of a Sponge City: Comprehensive Assessment of the Vulnerability of an Urban Rainwater System

This study introduces the concept of urban rainwater system vulnerability and identifies the indicator factors that affect the vulnerability of rainwater systems. Using the analytic hierarchy process (AHP), an index system for the vulnerability assessment of the rainwater system was established, and a vulnerability assessment model for the rainwater system was constructed. By integrating vulnerability degree, recurrence period, and water depth of ponding, a vulnerability assessment framework for urban rainwater systems was developed. Taking a newly developed urban area in the Suzhou High-Tech Zone as an example, we calculated the vulnerability degree of the urban rainwater system in this area to be 0.6497, indicating a high level of vulnerability and poor system safety. When encountering rainfall with a recurrence period p > 5 years, the city is likely to experience severe waterlogging. Through the analysis and evaluation of the rainwater system’s vulnerability, while clarifying the current state of the rainwater system, it can provide a scientific reference basis for the system’s upgrade, transformation, and optimized operation and management. Although the selection of factors may not be entirely comprehensive, this method allows for adjustments based on the composition and operation of different rainwater systems.

Enhancing Financial Vulnerability Models for Non-Profit Organizations: Integrating Financial Ratios and Macroeconomic Indicators for Comprehensive Risk Assessment

Purpose: This research advances the financial vulnerability prediction model for Non-Profit Organizations (NPOs) by incorporating a comprehensive set of financial and macroeconomic indicators, aiming to refine risk management and strategic planning within the sector. Theoretical Framework: Building upon Tuckman and Chang (1991) foundational work, this study addresses the evolving financial landscape's complexities. It expands on traditional financial ratio analyses to include broader economic conditions affecting NPOs, recognizing the limitations of previous literature. Design/Methodology/Approach: The study analyzes financial data from two NPOs (2017-2021), using three models to assess the influence of internal financial ratios (e.g., Debt Ratio, Cash Ratio, Net Operating Margin) and external macroeconomic factors (e.g., inflation, GDP growth) on NPO financial vulnerability and performance. Findings: Results highlight a significant positive relationship between the Cash Ratio and Return on Assets, underscoring liquidity management’s importance for NPO sustainability. Other factors showed minimal impact, suggesting a nuanced approach to financial vulnerability. Research Implications: The findings emphasize the necessity of integrating financial ratios with macroeconomic indicators in NPO management strategies, offering a refined model for understanding and mitigating financial risk. Originality/Value: This study enhances the financial management literature for NPOs by incorporating macroeconomic indicators into vulnerability assessment models. It provides a comprehensive framework for robust financial planning, filling a crucial gap in non-profit financial analysis and contributing to the sector’s resilience.

Application of the fuzzy analytic hierarchy process for water resources in the Wadi AlHasa catchment, Jordan

This article introduces a groundwater vulnerability assessment model that utilises the fuzzy analytic hierarchy process (FAHP) in the Wadi AlHasa catchment, Jordan. The assessment takes into account both geomorphological and hydrogeological variables, employing a comprehensive methodology that integrates various parameters. To evaluate the catchment, the study employs remote sensing and Geographic Information System (GIS) techniques. The analysis of the digital elevation model enables the creation of a map illustrating the diverse geomorphology of the catchment. This geomorphology significantly influences drainage density, direction, and the spatial distribution and intensity of flash flood events. Moreover, the study develops and maps a fuzzy FAHP DRASTIC vulnerability index, which proves to be a valuable tool for assessing the susceptibility of groundwater resources to contamination. The unique feature of the index is its ability to incorporate uncertain or subjective data, providing a means to evaluate the significance of various influencing factors. This information serves as critical support for decision-making and management efforts geared towards safeguarding and enhancing groundwater resources. Within the study area, the DRASTIC vulnerability index values span from 0.08325 to 0.28409, with 18% of the site exhibiting a high vulnerability rate. Additionally, the article implements a managed aquifer recharge model (MAR), with 31% of the area falling into MAR classes. Among these, 22.1% are classified as a high MAR class, while 0.7% belong to a very high MAR class. These findings underscore the feasibility of MAR projects in regions with limited water resources.

Spatio-Temporal Pattern and Vulnerability Assessment of the Human Settlements along the Beibu Gulf Coast of Guangxi, China

Human settlement development is intricately linked to production and life, and development quality is a partial indicator of the local community’s social, economic, cultural, and environmental progress. Furthermore, the human settlements’ vulnerability affects their sustainable development potential. Due to the “One Belt, One Road” initiative and land and sea corridor development in southwestern China, the Beibu Gulf coast in Guangxi is the sole waterway bridgehead used by China for opening trade with ASEAN. By examining the region’s human settlements and its potential for sustainable development, we established a benchmark for the region’s seaward economy and human settlements to achieve synergistic progress, as well as to enhance regional integration. Based on the systematic evaluation of the quality of human settlements along the Beibu Gulf coast in Guangxi, the vulnerability assessment model of human settlement was adopted to analyse the vulnerability and spatial–temporal patterns of nearby human settlements in 2010–2019, while the multiple linear regression model was used to explore their influencing factors. Our findings indicate that the quality of human settlements in the study area has experienced three distinct stages. In the early stage (2010–2012), we noted an overall year-on-year increase in settlement quality. However, in the middle stage (2013–2016), Fangchenggang and Beihai experienced a decline followed by an increase, while Qinzhou demonstrated a steady growth in settlement quality. Qinzhou continued to experience growth, albeit at a slower rate, while Fangchenggang’s rate of increase surpassed that of Beihai. The human settlements’ high-quality centre shifted between Qinzhou and Fangchenggang in the late stage (2017–2019). Additionally, in recent years, the settlements’ high-quality centre has shifted towards the western part of the region. The habitat’s sensitivity has grown slowly, as has the responsiveness index, but the vulnerability index continues to decline, and it is characterised by a spatial differentiation ranging from low to high values in a centre–east–west direction. The region’s human settlement quality is primarily influenced by economic development. The region’s habitat vulnerability is primarily caused by responsibility, and the primary factors affecting responsibility are regional imbalances in social service development, the population’s welfare, and employment and infrastructure.

Integrated SEAWAT model and GALDIT method for dynamic vulnerability assessment of coastal aquifer to seawater intrusion

Seawater intrusion (SI) has become a global issue exacerbated by intense anthropogenic activities and climate change. It is imperative to seek a synergistic strategy to reconcile environmental and economic benefits in the coastal regions. However, the intricate SI process and data scarcity present formidable challenges in dynamically assessing the coastal groundwater vulnerability. To address the challenge, this study proposed a novel framework that integrates the existing vulnerability assessment method (GALDIT) and variable-density groundwater model (SEAWAT). The future scenarios from 2019 to 2050 were investigated monthly under climate change (SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5) and human activities (80 % and 50 % of current groundwater abstraction) in Longkou city, China, a typical coastal region subject to extensive SI, compared with the status quo in 2018. Results indicated that by 2050, the high vulnerability area, is in a narrow buffer within 1.2 km from the shoreline and exhibits minor changes while the salt concentration here increased by about 2700 mg/L compared with the current situation. The moderate vulnerability zone expands by about 30 km2, and the low vulnerable area decreases proportionally. The groundwater over-abstraction is identified as a more critical factor compared to the regional precipitation under climate change. When groundwater abstraction is reduced to 80 % of the current scale, the expansion rate of the moderate-vulnerable area slows down significantly, with an expansion area of only 18 km2 by 2050. Further reducing groundwater abstraction to 50 % of the current scale shifts the evolution trend of the medium-vulnerable area from expansion to contraction, with the area shrinking by about 11 km2 by 2050. The integrated vulnerability assessment framework can be applied not only in the similar coastal regions but also provides insights into other natural hazards.

Vulnerability assessment and analysis of cryosphere changes in the Western Sichuan Plateau

ABSTRACT A vulnerability assessment model and indicator system for the changes in the cryosphere in the Western Sichuan Plateau are constructed based on the “exposure–sensitivity–adaptive capacity” framework. The model aims to capture the multiple characteristics of vulnerability to cryosphere changes in the region by selecting influencing factors from three dimensions: exposure, sensitivity, and adaptive capacity. Furthermore, the study explores the interrelationships among vulnerability dimensions and employs geographical detectors to analyze the driving mechanisms of vulnerability. The ultimate goal is to provide a scientific and rational basis for developing effective adaptation strategies to prevent and mitigate cryosphere-related disasters in the area. The main findings are as follows: (1) The vulnerability to cryosphere changes in the Western Sichuan Plateau exhibits a distribution pattern characterized by higher vulnerability in the west and lower vulnerability in the east. (2) There is a significant correlation between vulnerability and geographical location, characterized by two types of distribution patterns: high–high aggregation and low–low aggregation. The aggregation patterns are relatively stable but exhibit spatial heterogeneity. (3) The vulnerability to cryosphere changes in the Western Sichuan Plateau is the combined result of natural geographical conditions, socioeconomic development levels, and other factors.