Search Results (1,338)

Seawater intrusion (SWI) represents an increasingly critical challenge for coastal groundwater systems, with particularly pronounced impacts observed along the Indian coastline. Coastal aquifers constitute a vital freshwater resource supporting domestic, agricultural, and industrial demands. This study evaluates SWI vulnerability along the entire Indian coast, extending from Gujarat to West Bengal, covering approximately 7517 km of shoreline and up to 100 km inland. The assessment applies the GALDIT vulnerability framework that combines several hydrogeological and hydrochemical criteria such as groundwater occurrence, aquifer hydraulic conductivity, depth to groundwater, distance from shoreline, hydrochemical data, and groundwater quality data. We also assessed the intrusion of existing seawater, shoreline location, and aquifer thickness. However, conventional vulnerability assessments are inherently static and often fail to capture anthropogenic influences. To address this limitation, the present study integrates multi-temporal land use and land cover (LULC) datasets derived from ESA WorldCover remote sensing data for the period 2017–2024. Incorporating LULC dynamics enables a more comprehensive evaluation of the impacts of urban expansion and agricultural intensification on coastal susceptibility to SWI. Accordingly, a modified GALDIT-LU framework is developed to assess the spatiotemporal evolution of coastal vulnerability. The outcomes suggest that huge parts of the Indian coastline are vulnerable to moderate or very high classes, with the very high vulnerability class growing from 13,295 km² in 2017 to 38,257 km² in 2024, a 188% increase in vulnerability over the course of seven years. Groundwater chloride concentrations from Central Ground Water Board (CGWB) monitoring well locations have been used for validation over the proposed assessment, and show good spatial agreement between areas identified as high vulnerability and the spatial distribution of groundwater salinity for all three assessment periods, lending support to the robustness and predictive power of the proposed groundwater salinity assessment. The findings carry direct implications for the United Nations 2030 Agenda, demonstrating that the identified vulnerability patterns intersect with critical targets related to clean water and sanitation, food security, public health, climate action, and poverty reduction along one of the world’s most densely populated coastlines. Full article

Data-driven models are increasingly used for flood and landslide susceptibility mapping in rapidly urbanizing regions, particularly in the Global South. In this context, land use and land cover (LULC) is routinely adopted as a conditioning factor, although its geomorphological meaning and temporal consistency with hazard inventories are seldom evaluated. Information Value (IV) models have been widely applied to landslide susceptibility, but their use for flood susceptibility in complex coastal cities remains limited. This study evaluates IV-based flood and landslide susceptibility in Vitória, Brazil, a predominantly insular city characterized by sharp geomorphological contrasts and high population density. Two LULC datasets with different levels of urban detail were tested as conditioning factors alongside topographic and hydrological variables. Eighteen models were constructed for each hazard and validated using Area Under the Curve (AUC) metrics and expert judgement. Morphological attributes were the most important predictors: slope alone achieved an AUC of 0.90 for landslides, whereas elevation reached 0.71 for floods. The inclusion of LULC increased AUC values to 0.94–0.95 for landslides and 0.85–0.89 for floods, but also introduced spatial and temporal biases associated with stationary and coarsely classified land cover. Our findings highlight the limitations of incorporating LULC as a conditioning factor without temporal harmonization with hazard inventories or adequate urban class disaggregation. We argue that, in complex urban settings, LULC is more appropriately interpreted as a proxy for exposure and vulnerability than as a dominant predisposing factor, and that its use in predictive models should be critically assessed to avoid misleading conclusions. Full article

Rapid urbanization is reshaping land surface conditions and local thermal environments in fast-growing coastal cities. This study examines how Land Use Land Cover (LULC) transformation influenced seasonal land surface temperature (LST) dynamics in Chattogram City Corporation (CCC), Bangladesh, over 2004–2024. Multi-temporal Landsat imagery was analyzed using a Random Forest classifier, and spectral indices (NDVI, NDBI, NDBaI, MNDWI) were derived to characterize surface biophysical conditions. Built-up land expanded by 27.71 km², largely replacing agricultural and vegetated areas. Summer mean LST rose from 36.08 °C to 36.50 °C, while winter LST rose from 25.25 °C to 26.97 °C. Only the winter warming trend is statistically significant; the summer change falls within the ±1–2 °C retrieval uncertainty of Landsat-derived LST. The summer–winter thermal gap consequently narrowed from 10.83 °C to 9.53 °C, indicating that urbanization-driven warming in this tropical coastal city is disproportionately concentrated in the cool dry season. Partial correlation and multiple regression analyses confirm that built-up intensity (NDBI) is the dominant driver of surface warming, while vegetation (NDVI) exerts a consistent cooling influence. Water bodies showed contrasting seasonal trends, with winter extent declining alongside a slight summer increase. These findings highlight the critical role of vegetation and water bodies in moderating urban heat and provide data-driven insights for climate-responsive planning in rapidly urbanizing coastal cities. Full article

The escalating global contradiction between water supply and demand has imposed novel imperatives on regional water security within the paradigm of resilient city development. To elucidate the supply–demand dynamics of water provisioning services under heterogeneous institutional contexts, this study selected the Guangdong–Hong Kong–Macao (GHKM) region as the empirical study area. Employing the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, we coupled meteorological, land-use/land-cover (LULC), and pedological data to quantify the provisioning of water yield services in 2024. Concurrently, sector-specific water demands—encompassing agricultural, industrial, domestic, and ecological categories—were accounted for using statistical yearbooks. Subsequently, a Supply–Demand Index (SDI) was formulated to delineate the spatial matching patterns. The findings reveal pronounced spatial heterogeneity in water service provisioning; high-value zones are predominantly aggregated along the western and southern coastal belts, whereas low-value zones are dispersed across the northern mountainous terrains. Based on the SDI classification, the study area comprises 12 supply-surplus, 6 supply–demand-equilibrium, and 5 supply-deficit administrative units. Notably, the northern Guangdong mountainous region assumes a critical ecological role in water conservation, whereas the core megalopolises of the Pearl River Delta exhibit an acute dependency on extrinsic water subsidies, with Macao demonstrating a distinct ecological deficit in water provisioning. Furthermore, this study uncovers the overestimation artifact of water yield estimations over urban impervious surfaces, thereby providing a robust empirical foundation for the cross-regional synergistic governance and adaptive management of water resources. Full article

Although recreational experience segmentation is widely used in coastal tourism research, existing motivational frameworks have been developed primarily in island and protected-area destinations, leaving urban, open-access, developing-country contexts theoretically underexplored. This study addresses that gap by identifying the dimensions of recreational experience and market segments for the city of Salinas, Ecuador—an accessible, family-oriented, urban coastal destination—to advance sustainable tourism management. Using a quantitative, cross-sectional design, data were collected through a structured questionnaire administered to 385 tourists; 374 cases with complete scale responses were retained for analysis. An exploratory factor analysis (EFA) identified seven motivational dimensions: environment and sustainable recreational resources, learning and knowledge, affective well-being and creative harmony, interest and enjoyment in the experience, aquatic natural resources and wildlife, cognitive stimulation and emotional connection, and hedonic well-being. A K-means cluster analysis, validated through the elbow method (inflection at k = 3) and silhouette coefficient (0.25), revealed three distinct tourist segments: intensive recreational tourists (n = 183; 48.9%), moderate recreational tourists (n = 136; 36.4%), and passive recreational tourists (n = 55; 14.7%). Chi-square tests confirmed significant associations between clusters and sociodemographic variables including gender, marital status, and number of visits. This study offers four theoretical contributions: it reports a more detailed motivational factor structure than previously documented three- and four-factor models for coastal destinations; it proposes recreational amotivation—grounded in self-determination theory—as a candidate visitor profile that merits further theoretical and empirical validation; it advances a destination-type-sensitive motivational framework; and it provides boundary condition evidence clarifying the contextual scope of recreational experience theory. These findings offer actionable segmentation tools for destination managers seeking to differentiate and sustain coastal tourism markets. Full article

Urban Heat Island (UHI) effects are increasingly affecting Mediterranean coastal cities, where climate change, urbanization, and seasonal tourism intensify thermal stress and environmental vulnerability. In this context, climate-oriented planning and green infrastructure are recognized as key strategies for urban adaptation. This study investigates the spatiotemporal evolution of Land Surface Temperature (LST) and vegetation cover in the coastal municipality of Lignano Sabbiadoro (northeastern Italy) through the analysis of Landsat imagery acquired between 1984 and 2023. Summer LST and Normalized Difference Vegetation Index (NDVI) maps were derived from June–August observations and used to assess long-term thermal dynamics, vegetation patterns, and Urban Heat Island development. Meteorological data indicate a significant increase in mean annual air temperature, with a warming trend of approximately 0.57 °C per decade between 1984 and 2023. Correspondingly, Landsat-derived LST maps reveal a marked intensification of summer surface temperatures, with mean summer LST increasing from 32.16 °C in 1984–1993 to a peak of 35.91 °C in 2004–2013, followed by a slight decrease to 35.77 °C during 2014–2023. During the same period, the proportion of municipal surfaces characterized by temperatures above 35 °C increased from 10.7% to more than 60%, while cooler areas (<30 °C) declined from 17.7% to 2.3%. The comparison between LST and NDVI patterns revealed a persistent inverse relationship between vegetation cover and surface temperature, with coastal pinewoods, green spaces, and water bodies consistently exhibiting lower thermal values than densely urbanized sectors. A key methodological contribution of the study is the operational integration of satellite-derived thermal remote sensing into the Green Plan of Lignano Sabbiadoro. LST mapping was used to identify priority areas for climate adaptation measures, including ecological corridors, wooded landscape connections, urban green corridors, and depaving interventions. The results demonstrate how multi-temporal thermal analysis can support evidence-based planning by linking climate assessment with the spatial prioritization and design of green infrastructure strategies. The proposed workflow provides a transferable framework for integrating remote sensing into climate-informed planning processes in Mediterranean coastal cities and other urban contexts increasingly exposed to heat-related risks. Full article

With the accelerated advancement of smart city construction, the development of urban underground space in coastal areas faces significant safety challenges due to randomly distributed granite boulders. Conventional geophysical methods often struggle to identify these heterogeneous bodies efficiently. This study proposes a comprehensive detection approach fusing the Cone-based Transient Electromagnetic Method (CTEM) and Microtremor Array Surveying. Taking the eastern coast of Jiaozhou Bay, Qingdao, as the research area, we integrated borehole data to validate the geophysical interpretation. The results demonstrate that the joint inversion accurately delineates four stratigraphic interfaces with depths consistent with borehole logs: the artificial fill (bottom at ~8–12 m), Quaternary sediments (~28–33 m), strongly weathered granite (~59–74 m), and moderately weathered granite. Specifically, boulders within the Quaternary and strongly weathered layers are distinctly identified by dual high-value anomalies: high apparent resistivity (>220 Ωm, approximately 1.5–2 times that of the surrounding rock) and high shear-wave velocity (>660 m/s). Furthermore, the method effectively differentiates boulders from water-rich fractured zones, which exhibit contrasting low resistivity (<50 Ωm) and low shear-wave velocity (<420 m/s). These quantitative findings confirm that the fused CTEM and microtremor technique provides precise spatial localization and reliable identification of boulders in complex coastal geological environments. Full article

Urban surface water networks are closely associated with the urban land surface thermal environment, but the spatial non-stationarity of their relationships with land surface temperature (LST) remains insufficiently understood. This study examined relationships between water network indicators and LST in Fuzhou. Following scale-sensitivity analysis to select the analytical grid scale, this study used global correlation and regression analyses to identify key indicators and applied multiscale geographically weighted regression (MGWR) to examine spatial non-stationarity in their relationships with LST. Based on the selected 150 m grid scale, the results showed the following: (1) Water coverage ratio (WCR), water body shape index (SI), and water network density (D) were retained as key explanatory variables, with WCR showing the strongest negative association with LST. (2) The relationships between water network indicators and LST showed spatial non-stationarity. WCR showed the most stable negative local association with LST, especially in built-up areas with higher background heat loads, whereas the local associations of SI and D were more context-dependent. These findings suggest that thermal-environment-oriented planning for urban surface water networks should prioritize water coverage while considering water network structure, water body morphology, the surrounding built environment and land cover conditions. These associations can inform spatially differentiated planning of urban surface water networks in comparable subtropical coastal cities. Full article

Earthquake-induced soil liquefaction represents a severe geohazard causing catastrophic infrastructure failure in prone coastal zones, requiring an advanced environmental spatial assessment for their sustainable land-use planning. This study utilizes advanced computational intelligence models to predict earthquake-induced soil liquefaction in Boumerdès, Algeria, an area heavily affected by the 2003 (M_w 6.8) earthquake. Utilizing a comprehensive subsurface database of 1984 geotechnical records encompassing lithology, hydrogeological configurations, and seismic parameters, advanced deep learning frameworks are developed and optimized via automated Neural Architecture Search (NAS). The continuous Factor of Safety (F_s) is calculated to distinguish stable profiles from vulnerable strata, benchmarking conventional ANN and DNN models against NAS-optimized variants (NAS-ANN and NAS-DNN) using a stratified 5-fold cross-validation scheme. The optimized hybrid NAS-DNN framework effectively captured non-linear soil responses, achieving a training correlation coefficient ($R_{train}$) of 0.9518, a validation coefficient ($R_{validation}$) of 0.8843, and a cross-validated mean $R$ of approximately 0.82, demonstrating improved predictive reliability compared to traditional models. Ultimately, this optimal network is embedded into the ‘GeoLiquefy-AI (v1.0)’ interface. To ensure reliability for safety-critical applications, we integrated a SHAP explainable AI framework, validating the model’s geomechanical logic by mapping physical soil-liquefaction dependencies. This deployment-ready tool enables rapid, transparent hazard calculations, providing a scalable platform for seismic microzonation and proactive urban risk mitigation. Full article

Urban flood resilience depends not only on the scale of infrastructure investment, but also on whether such investment can be translated into observable flood-mitigation outcomes. Focusing on the transformation from infrastructure response to flood outcomes, this study uses panel data for 21 prefecture-level cities in Guangdong Province from 2016 to 2022. Annual maximum monthly precipitation is used to represent rainfall pressure, stormwater pipe density to represent infrastructure scale, and the number of reported flood events to represent the outcome variable. A two-way fixed-effects Poisson pseudo-maximum likelihood (PPML) model is employed. The results show that, in the full sample, rainfall pressure is positively, but not significantly, associated with reported flood occurrence, while stormwater pipe density does not exhibit a stable negative moderating effect. The main conclusion remains broadly unchanged when alternative outcome and precipitation indicators are used, when pipe density is lagged by one period, and when a conservative sample is adopted. Extended analysis provides only limited weak negative evidence for Pearl River Delta cities, and this evidence is not robust across alternative specifications. The findings indicate that pipe length per unit of built-up area primarily reflects the scale of infrastructure provision and cannot be directly equated with the operational performance of the drainage system. By separating response inputs from outcome performance, this study reveals the conditional nature of the transformation from infrastructure scale to operational performance in urban flood resilience research and provides empirical support for a shift from infrastructure expansion toward performance-oriented and integrated governance in high-density coastal cities. Full article

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a densely populated, highly connected coastal urban region that is exposed to typhoons, extreme rainfall, flooding, storm surges, heat-related risks, and public health emergencies. Existing studies have clarified many aspects of hazard exposure and spatial vulnerability, but evidence on how residents receive warnings, interpret risks, prepare, evacuate, and contribute to community resilience remains dispersed across disciplines and jurisdictions. This study maps the intellectual structure and thematic evolution of research on residents’ responses to urban disaster risks in the GBA. Following a PRISMA 2020-aligned identification and screening process, 144 records from the Web of Science Core Collection were manually screened. Bibliometric analysis, keyword co-occurrence analysis, a region–hazard matrix, and a BERTopic-inspired topic modelling procedure were then used to identify publication trends, disciplinary sources, spatial hazard patterns, and latent themes. The results show accelerated growth after 2020 and identify six interrelated themes: risk perception and preparedness; evacuation and shelter accessibility; urban flooding vulnerability; typhoon, storm surge, and coastal community risk; community resilience and climate adaptation governance; and health emergency response and psychosocial resilience. The review synthesises these findings into a heuristic framework linking risk information, cognitive appraisal, response action, and community resilience. The GBA is treated as an analytically informative, rather than statistically representative, case of a high-density, coastal, and multi-jurisdictional urban region. The findings suggest that resident-centred resilience planning should connect trusted and actionable warnings with inclusive digital communication, accessible protective resources, and cross-boundary coordination for compound hazards. Full article

The tourism sector in the Sultanate of Oman is central to “Oman Vision 2040”, with a strategic focus of the government on its dynamic transformation. Coastal regions, vital to tourism, are affected by changes to the coastline due to flash floods, sea-water flooding, and erosion. Despite its implications for tourism and the economy, this topic remains relatively under-explored, especially as to use of Sentinel-1 satellite images. This study assesses water-level changes due to erosion in the urban coastal region of the Al-Batinah governorate via land cover classification. Using the Support Vector Machine (SVM) classification technique, the overall accuracy is found to be 97.7% and the Kappa coefficient value for the year 2018 is 1.0. Although, when using the Random Forest (RF) classification technique, the accuracy is nearly identical, there is varying precision for the water area. A critical observation is made, showing significant increase of the water area from 2.99% in the year 2017 to 12.36% in the year 2025, suggesting water encroachment. With fixed-effect and combined-effect size meta-analysis models, the confidence levels were identified as 95.0% and 0.37, respectively, indicating a consistent variation in water area that supports the outcomes of image classification. This study offers a valuable insight for policymakers as to managing coastal regions, along with providing assistance to vulnerable coastal communities. The study focuses on a particular governorate, given the satellite images, whereas a broader regional comparison would address the limitation of the generalizability of results. In the future, the research could integrate surveys from coastal communities and businesses for a comprehensive qualitative data perspective on the region’s tourism sector. Full article

Mangrove forests provide essential ecosystem services but are increasingly threatened by anthropogenic pressures and climate-related disturbances. Effective and accessible tools for assessing mangrove ecosystem condition are therefore needed to soundly support their conservation and management. This study adapted the “Coastal Health” framework originally proposed for assessing coastal ecosystems health to evaluate the health status of mangrove forests along the Caribbean coast of Colombia. Using freely available high-resolution imagery from Google Earth Pro 7.3, complemented by field observations, technical reports, and unpublished literature, 56 mangrove sites distributed across eight coastal departments were assessed according to their ecological integrity, hydrological connectivity, sediment dynamics, freshwater and marine inputs, mangrove species condition, and their potential for landward and seaward migration. The results showed that 54% of the evaluated sites were classified as being in “Good Health”, while 2% were categorized as “Health Warning”, 3% as “Surface Wounds”, 14% as “Minor Injury”, 25% as “Major Injury”, and 2% as “Deceased”. Mangroves in good condition were generally associated with protected areas, river mouths, estuaries, and relatively isolated coastal systems, whereas degraded ecosystems were affected by urban expansion, tourism infrastructure, hydrological alterations, coastal engineering works, and reduced freshwater inputs. The proposed methodology proved to be a simple, low-cost, and easily replicable tool for large-scale mangrove health assessment. It provides valuable information for prioritizing conservation and restoration actions and can be adapted to support mangrove monitoring and ecosystem-based coastal management in other regions worldwide. Full article

Accelerated urbanisation and associated land-use conversion are reshaping the composition and functions of terrestrial ecosystems globally. In Southeast Asia, ecological change is increasingly mediated not only by demographic urbanisation but also by urban expansion, peri-urban development, and the conversion of agricultural, coastal, and forest land into built-up surfaces. This study integrates multi-source geographical information from 2014 to 2024 to examine 351 provincial-level units in 11 Southeast Asian nations. To describe the spatial-material dimension of urbanisation and ecological conditions, two indices were created: the Composite Nighttime Light Index (CNLI), used as a proxy for urban expansion and built-up development intensity, and the Improved Remote Sensing Ecological Index (IRSEI), which is tailored to tropical coastal locations. The development of human-environment interactions was measured using the Coupling Coordination Degree (CCD) model. Pathways of synergy and trade-off were found using an incremental four-quadrant framework, and nonlinear causes of spatial differentiation were investigated using Spearman correlation and the Optimal Parameter-based Geographical Detector (OPGD). Uncertainty was addressed through data-quality masking, annual compositing, consistent index-construction rules, and cautious interpretation of CCD and driver results as relative provincial-scale patterns. The regional mean CCD rose from 0.250 to 0.314 during the decade, showing a slow improvement; nevertheless, most places still have low to moderate levels of coordination. There is clear pathway divergence, with 38.7% of locations enduring trade-offs where built-up development happens at the price of ecological quality and 58.4% of regions seeing synergistic improvement. The coupling pattern is primarily driven by built-up area expansion, with multiple factors jointly producing strong nonlinear enhancement effects. Climate conditions and forest disturbance further strengthen these effects. This study extends beyond single-country analyses by situating remote-sensing coupling results within land-use transition, peri-urbanisation, urban–rural linkage, and regional-governance perspectives. It provides quantitative evidence to support differentiated policy strategies in rapidly urbanising places. Full article

Using daily rainfall observations from 1961 to 2022 (62 years) across 15 typical sub-regions within the water-receiving area of the Zhejiang East Water Diversion Project, this study systematically examines the spatiotemporal distribution, long-term trends, multi-scale variability, and inter-regional correlations of rainfall. An integrated framework was employed, including the Mann–Kendall (MK) trend test, Sen’s slope estimator, Hurst exponent analysis, and multi-scale sliding window analysis. Spatially, the multi-year average daily rainfall ranges from 3.47 mm to 5.68 mm, following a distinct “high in the center, low in the west” pattern, with the Yuyao Plain Mazhu Midstream Area identified as the regional rainfall maximum. In the raw Mann–Kendall test all 15 sub-regions exhibit increasing trends, of which 14 remain statistically significant (p < 0.05) after trend-free pre-whitening; Sen’s slopes follow a “coastal > riverine > hilly” gradient. Hurst exponents greater than 0.5 suggest persistent behavior in the rainfall series, although attribution to external forcing or separation from low-frequency climate variability requires additional analysis. Multi-scale sliding window analysis reveals strong scale dependence: the amplitude of trend fluctuations decreases by approximately 90% from ±16 mm at the 3-month scale to ±1.7 mm at the 12-month scale, while inter-regional correlation coefficients increase from 0.83 to 0.87. Notably, the Yuyao Plain Mazhu Midstream Area displays a unique “increase-then-decrease” correlation pattern, reflecting its distinctive hydro-geographic conditions. These findings provide a scientific basis for climate-adaptive scheduling of water diversion projects, supporting water security, urban resilience, and climate action. Full article