Search Results (204)

Tropical cyclones (TCs) over the Arabian Sea pose significant threats to coastal populations and result in substantial economic losses, yet their variability in response to major climate modes remains insufficiently understood. This study examines the relationship between the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Indo-Pacific Warm Pool (IPWP) with TC activity over the Arabian Sea from 1982 to 2021. Utilizing the India Meteorological Department (IMD)’s best-track data, reanalysis datasets, and composite analysis, we find that ENSO and IOD phases affect TC activity differently across seasons. The pre-monsoon season shows a limited association between TC activity and both ENSO and IOD, with minimal variation in frequency, intensity, and energy metrics. However, during the post-monsoon season, El Niño enhances TC intensity, resulting in a higher frequency of intense storms, leading to increased accumulated cyclone energy (ACE) and power dissipation index (PDI) in a statistically significant way. In contrast, La Niña favors the development of weaker TC systems and an increased frequency of depressions. While negative IOD (nIOD) phases tend to suppress TC formation, positive IOD (pIOD) phases are associated with increased TC activity, characterized by longer durations and higher ACE and PDI (statistically significant). Genesis sites shift with ENSO: El Niño favors genesis in the eastern Arabian Sea, causing westward or northeastward tracks, while La Niña shifts genesis toward the central-western basin, promoting northwestward movement. Composite analysis indicates that higher sea surface temperatures (SSTs), reduced vertical wind shear (VWS), increased mid-tropospheric humidity, and lower sea level pressure (SLP) during El Niño and pIOD phases create favorable conditions for TC intensification. In contrast, La Niña and nIOD phases are marked by drier mid-level atmospheres and less favorable SST patterns. The Indo-Pacific Warm Pool (IPWP), particularly its westernmost edge in the southeastern Arabian Sea, provides a favorable thermodynamic environment for genesis and exhibits a moderate positive correlation with TC activity. Nevertheless, its influence on interannual variability over the basin is less significant than that of dominant large-scale climate patterns like ENSO and IOD. These findings highlight the critical role of SST-related teleconnections (ENSO, IOD, and IPWP) in regulating Arabian Sea TC activity, offering valuable insights for seasonal forecasting and risk mitigation in vulnerable areas. Full article

Traditional single-species fisheries management has proven inadequate for capturing ecosystem interactions, leading to a shift toward ecosystem-based approaches. In Korea, diverse small- and medium-scale with varying gear types, production volumes, and practices require management tools that address both ecological and industrial needs. This study developed a Gear-based Fisheries Management Index (GFMI) for 24 coastal and offshore fisheries in Korea. The framework, based on the “ideal gear attributes” defined by ICES, is structured around three objectives: gear controllability, environmental sustainability, and operational functionality. Sub-indicators and weights were derived through expert consultation using the Analytic Hierarchy Process and standardized with Z-scores from national statistics, including production volume, license numbers, and accident rates. Results show that in coastal fisheries, coastal gillnets (61.7) and coastal improved stow nets (60.7) recorded the highest scores, largely due to negative impacts such as bycatch, reproductive capacity, and gear loss. Coastal purse seines (40.9) received the lowest score, reflecting species selectivity advantages. In offshore fisheries, large bottom pair trawls (71.8) and Southwestern medium-size bottom pair trawl (69.3) ranked highest, indicating strong habitat impacts. While coastal improved stow nets, large purse seines, and large trawls performed well in operational functionality, high costs and efficiency constraints remain key vulnerabilities. Full article

This study develops a computational framework for the simultaneous quantification of seismic resilience and economic losses in corrosion-affected coastal continuous girder bridges. The proposed model integrates adjustment factors to reflect delays in post-earthquake repairs and cost increments caused by progressive material degradation. Finite element methods and nonlinear dynamic time-history simulations were conducted on an existing coastal continuous girder bridge to validate the proposed model. The key innovation lies in a probability-weighted resilience index incorporating damage state occurrence probabilities, which overcomes the computational inefficiency of traditional recovery function approaches. Key findings demonstrate that chloride exposure duration exhibits a statistically significant positive association with earthquake-induced structural failure probabilities. Sensitivity analysis reveals two critical patterns: (1) a 0.3 g PGA increase causes a 11.4–18.2% reduction in the resilience index (RI), and (2) every ten-year extension of corrosion exposure decreases RI by 2.7–6.2%, confirming seismic intensity’s predominant role compared to material deterioration. The refined assessment approach reduces computational deviation to ±2.4%, relative to conventional recovery function methods. Economic analysis indicates that chloride-induced aging generates incremental indirect losses ranging from $58,000 to $108,000 per decade, illustrating compounding post-disaster socioeconomic consequences. This work systematically bridges corrosion-dependent structural vulnerabilities with long-term fiscal implications, providing decision-support tools for coastal continuous girder bridges’ maintenance planning. Full article

This study aims to develop a climate-resilient landscape design framework for coastal healthcare facilities by integrating Artificial Intelligence (AI)-generated design prompts with Computational Fluid Dynamics (CFD) simulations and on-site validation. Focusing on a coastal hospital in Penghu, Taiwan—a region vulnerable to strong winds, salt spray, and extreme weather—the research proposes a climate-adaptive, microclimate-responsive, and resilient design framework. Key findings demonstrate that the optimized design reduced average winter wind speed from 12 m/s to 4.5 m/s (a 62.5% reduction) and increased the three-year survival rate of salt-tolerant plant species (e.g., Pittosporum tobira, Casuarina) to 92%, significantly outperforming conventional planting strategies. The combination of water features and evapotranspiration planting reduced summer temperatures by 2.3 °C and increased humidity to 75%, with the PMV comfort index improving from +1.5 to +0.5. The program also resulted in a 15% increase in biodiversity, a 20% reduction in soil erosion, and a 40% improvement in users’ perceived aesthetic value of outdoor spaces. Furthermore, AI-based analyses to determine foundational depth led to a reduction in structural failure rates—from 40% to 5%—substantially elevating the safety and long-term durability of outdoor infrastructures. This study demonstrates that integrating AI with CFD is both feasible and highly effective for addressing complex coastal climate challenges in landscape architecture. The developed framework is parametric, evidence-based, and tailored to site-specific requirements, enabling the formulation of intelligent, climate-responsive landscape solutions for future healthcare environments in vulnerable coastal areas. Full article

Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development. Full article

Wetlands are natural ecosystems of great ecological and economic value. They provide undeniable ecosystem services that contribute to promoting sustainable development. Exposed to different pressures, these limnic ecosystems are particularly vulnerable to climate change. Thus, assessing wetland vulnerability is of utmost importance. Based on a systematic selection of relevant peer-reviewed studies, this paper helps to develop a general vision of the methods used to assess wetland vulnerability in different contexts, emphasizing the use of advanced computational approaches. Hence, an overview of different cases of wetlands all across the five continents and of different types of habitats is presented. Whether the wetland is permanently or seasonally flooded, coastal, or tropical, this study enables the analysis of diverse, already established vulnerability evaluation index systems. Some of these indices were computed using geographic information systems (GISs), artificial intelligence (AI), machine learning (ML), spatial principal component analysis (SPCA) and driver–pressure–state–impact–response (DPSIR) as evaluation models. Indeed, given the adoption of different methods, diverse models, and analytical approaches under different scenarios, the vulnerability assessment process should be seen as an iterative rather than a definitive process. An accurate wetland vulnerability assessment is essential for ensuring the sustainability of wetland ecosystems and for informing effective conservation and management strategies. Full article

The Arctic region, one of the most vulnerable areas globally, faces severe climate change impacts, with rising sea levels and temperatures threatening local communities. Modern geoinformation tools provide a reliable, cost-efficient, and time-saving method for assessing these climate changes in Arctic coastal regions. This study focuses on Finland’s Arctic and sub-Arctic diverse coastline. The Coastal Vulnerability Index (CVI) is used to assess the vulnerability of Finland’s coastlines, using advanced geoinformatics tools. Integrating high-resolution data from EMODnet, the National Land Survey of Finland Digital Elevation Model (DEM), and physical sources, the CVI includes six input parameters: geomorphology, coastal slope, shoreline change rates, mean wave height, tidal range, and relative sea-level change. The CVI results reveal pronounced spatial variability: 37% of the coastline is classified with very low vulnerability, primarily in the southern Gulf of Finland, and some northern segments, specifically part of Lapland, exhibit minimal susceptibility to coastal hazards. Conversely, the central Gulf of Bothnia shows high vulnerability (29%), with low and moderate vulnerability zones comprising 27% and 6%, respectively, and very high vulnerability at 1%. This assessment provides essential insights for sustainable coastal management in Finland by offering a replicable model for Arctic coastal assessments. This study supports policymakers and local communities in developing targeted adaptation strategies to enhance resilience against climate-driven coastal hazards. Full article

Temporal alterations in coastlines depict the significant changes in coastal areas, driven by both natural processes and human activities. For island nations, monitoring of the coastline is essential due to their vulnerability to such impacts. In this study, Funafuti Atoll, an archipelago of small and scattered islands around the capital of Tuvalu, is selected as the study region, and the aim is to extract coastlines of different islands and investigate coastal area changes between 2019 and 2023 using Sentinel-2 imagery. A simple linear iterative clustering-based superpixel segmentation and adaptive thresholding approach is employed for coastline extraction. Initially, superpixel segmentation is conducted to cluster 3-band image pixels into coherent regions, excluding the sea area. Subsequently, the normalized difference vegetation index (NDVI) is calculated, and the superpixels are used to obtain corresponding NDVI regions, on which adaptive Gaussian thresholding is applied to extract coastlines. Finally, the areas enclosed by the extracted coastline boundaries are utilized for change analysis. The results indicate that islands along the western rim of Funafuti exhibited significant alteration (an average decrease of −14.48%), whereas those along the eastern rim remained relatively stable due to the presence of coral rubble ridges and steep slopes. The change analysis revealed that from 2019 to 2020, approximately 15.1 hectares (ha) were eroded, resulting in a net area change rate of −4.14%. Between 2020 and 2021, erosion increased to 20.2 ha, yielding a net change of −7.75%. From 2021 to 2022, 13.2 ha were eroded, corresponding to a −1.74% change. From 2022 to 2023, a net gain of 10.3 ha occurred (+0.25%), primarily due to land reclamation along the lagoon-facing coast of Fongafale Island. Overall, all islands showed a decreasing area trend between 2019 and 2023, with an average net change of −12.97%. The coastal changes occurred along the sand-dominated coast with gentle slopes, possibly driven by the impact of tropical cyclones, prolonged swells, and coastal flooding, which act as the primary driving forces for the study region. 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

This study presents a comprehensive coastal vulnerability assessment framework by integrating a range of physical, environmental, and climatic parameters. Key criteria include shoreline changes, coastal geomorphology, slope, elevation, bathymetry, tidal range, wave height, shoreline change rates, population density, land use and land cover (LULC), temperature, precipitation, and coastal inundation factors. By synthesizing these parameters with real-time coastal monitoring data, the framework enhances the accuracy of regional risk evaluations. The study employs Multi-Criteria Spatial Analysis (MCSA) to systematically assess and prioritize vulnerability indicators, enabling a data-driven and objective approach to coastal zone management. The findings aim to support coastal planners, policymakers, and stakeholders in designing effective, sustainable adaptation and mitigation strategies for regions most at risk. This integrative approach not only strengthens the scientific understanding of coastal vulnerabilities but also serves as a valuable tool for informed decision-making under changing climate and socioeconomic conditions. Full article

As climate change intensifies, assessing vulnerability at territorial levels such as cities, countries, and regions is essential for effective adaptation planning. This study evaluates the applicability of the United Nations Environment Programme and South Pacific Applied Geoscience Commission’s Environmental Vulnerability Index (EVI) for coastal regions in South Korea. By adapting and localizing 50 international indicators and a Geographic Information System framework, this research developed a Korean Coastal Vulnerability Index and used spatial regression analysis to compare results to historical water-related disaster data from 2010 to 2019. The findings reveal that contrary to South Korea’s global classification of “extremely vulnerable”, most coastal counties appear relatively resilient when viewed through the localized model. Sub-index analyses indicate that ecological and anthropogenic damage factors show the strongest correlation with past disasters among the hazard, resistance, and damage categories. While the model’s explanatory power was modest (R² = 0.017), the regression nonetheless provides meaningful insight into how global indices can reflect local vulnerability patterns. The regression results confirm that based on historical hazard records, the international model effectively predicts Korean coastal vulnerability. It demonstrates the potential of scaling down global models to fit national contexts, offering a replicable approach for countries lacking localized vulnerability frameworks. It advances climate adaptation research through methodological innovation, policy-relevant spatial analysis, and theoretical insights into the multidimensional nature of vulnerability. The results support more precise, data-driven resilience planning and promote international collaboration in climate risk management. Full article

Heavy metal pollution is a growing public health concern owing to rising environmental pollution throughout the world. The situation is more vulnerable in Bangladesh; therefore, this study assessed contamination levels in different land use categories such as rural, local market, industrial, research, and coastal areas, as well as the related health risks for farmers in Bangladesh. A total of 45 soil samples were considered from three depths (0–5 cm, 5–10 cm, and 10–15 cm) across five different areas, with three replications per depth, following the monsoon season. Samples were prepared using a diacid mixture, and heavy metals (Cu, Ni, Mn, Cr, Zn, Pb) were investigated using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Health risks were evaluated using standard assessment models. The results showed that coastal agricultural soils had the highest heavy metal concentrations (except Pb), while rural areas had the lowest (except Cu and Ni), with no clear depth-based pattern. Two contamination sources were identified: component 1 (Cu, Ni, Mn, Cr, Zn) and component 2 (Pb, Zn), indicating mixed and anthropogenic sources, respectively. The Pollution Load Index (PLI) was highest in coastal areas and lowest in rural areas. The average daily intake of metals followed the order of inhalation > dermal > ingestion, with inhalation being the primary exposure route. The highest cumulative cancer risk (CCR) was observed in coastal agricultural soils (5.82 × 10⁻⁹), while rural soils had the lowest CCR (8.24 × 10⁻¹⁰), highlighting significant regional differences in health risks. Full article

The intensification of extreme temperature events driven by climate change has heightened the vulnerability of urban areas to heatwaves, making it a critical environmental challenge. In this study, we investigate the spatial characteristics of urban heatwave vulnerability in Jungang-dong, Gangneung—a representative mid-sized coastal city in South Korea that experiences a strong urban heat island (UHI) effect due to the prevalent land–sea breeze dynamics, high building density, and low green-space ratio. A representative heatwave day (22 August 2024) was selected using AWS data from the Korea Meteorological Administration (KMA), and hourly meteorological conditions were applied to Computational Fluid Dynamics (CFD) simulations to model the urban microclimates. The thermal stress levels were quantitatively assessed using the Universal Thermal Climate Index (UTCI). The results indicated that, at 13:00, the surface temperatures reached 40 °C and the UTCI values peaked at 43 °C, corresponding to a “Very Strong Heat Stress” level. Approximately 17.4% of the study area was identified as being under extreme thermal stress, particularly in densely built-up zones, roadside corridors with high traffic, and pedestrian commercial areas. Based on these findings, we present spatial analysis results that reflect urban morphological characteristics to guide the optimal allocation of urban cooling strategies, including green (e.g., street trees, urban parks, and vegetated roofs), smart, and engineered infrastructure. These insights are expected to provide a practical foundation for climate adaptation planning and thermal environment improvement in mid-sized urban contexts. Full article

Over the last decade, concern has increased about the deterioration of groundwater quality in coastal aquifers due to salinization processes resulting from uncontrolled abstraction and the impacts of global climate change. This study investigated the groundwater geochemistry of a narrow sandy peninsula bounded by the ocean and brackish water lagoons in northern Sri Lanka. The population of the region has grown rapidly over the last decade with increasing agricultural activities, and therefore, the use of groundwater has increased. To investigate the effects of seawater intrusion and anthropogenic activities, selected water quality parameters and water isotopes (δ²H and δ¹⁸O) were measured in 51 groundwater samples. The results showed that selected shallow groundwater wells are vulnerable to contamination from anthropogenic processes and seawater intrusion, mainly indicated by Cl/Br ratios. Iron-rich groundwater (0.11 to 4.2 mg/L) could represent another problem in the studied groundwater. According to Water Quality Index calculations, 41% of shallow wells contained poor and unsuitable water for domestic and irrigation purposes. Most of the groundwater in the region was saturated with Ca and Mg containing mineral phases such as calcite, dolomite, magnesite and gypsum. Water isotopes (δ²H and δ¹⁸O) showed that about 50% of the groundwater samples were scattered near the local meteoric water line. This indicates sufficient rainwater infiltration. However, some samples exhibit elevated isotope values due to seawater admixture and secondary evaporation under semi-arid conditions. This study showed the utility of Cl/Br ratios as indicators for distinguishing anthropogenic sources of Cl contributions to groundwater in shallow, permeable aquifer systems. 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