Search Results (641)

Urban floods have become a major systemic risk to sustainable urban development under climate change and increasingly frequent extreme hydro-meteorological events. Yet evidence on the long-term evolution of urban flood resilience (UFR) and its structural constraints at the provincial scale remains limited. This study develops a PSR-based framework to assess UFR and diagnose its dominant obstacles using data for 21 prefecture-level cities in Sichuan Province from 2011 to 2023, including meteorological, geomorphological, socioeconomic, infrastructure, environmental, and public service indicators. A combined AHP–EWM is used to integrate subjective and objective information, TOPSIS is applied to derive a composite UFR index and subsystem scores, and an obstacle degree model is employed to identify key constraints and their temporal evolution. Results show that: (1) UFR in Sichuan Province fluctuated but increased overall during 2011–2023, reaching its highest level in 2023; (2) resilience improvement was driven mainly by the response subsystem, while the pressure subsystem showed the greatest interannual variability; and (3) the annual top five obstacles were highly persistent and insufficient response capacity was the dominant long-term constraint on resilience enhancement. These findings underscore that improving the adequacy, institutional robustness, and operational stability of response systems is central to enhancing UFR. This study provides empirical support for the assessment of provincial-scale resilience and policy-oriented flood risk governance. Full article

This study presents a city-scale physical coastal vulnerability assessment of the 21 km Monrovia coastline (Liberia) using a multi-parameter coastal vulnerability index (CVI). Nine physical parameters—geology/geomorphology, shoreline change rate, elevation, slope, bathymetry, wave height, tidal range, relative sea level rise, and coastal landform characteristics—were integrated within an equal-weight ranking framework. The results identify spatially concentrated high vulnerability segments associated with low elevation, sandy geomorphology, and persistent shoreline retreat. The CVI represents a relative exposure screening rather than a predictive risk model. Limitations related to parameter weighting, classification dependency, and temporal heterogeneity are acknowledged. The findings support preliminary spatial prioritization for coastal adaptation planning Full article

This research presents a comprehensive multi-domain environmental assessment of Delos Island, a UNESCO World Heritage Site, through integration of long-term atmospheric and satellite remote sensing datasets. A significant methodological contribution of this research is the development of a cross-mission harmonization approach that enables the reconstruction of a continuous, multi-decadal atmospheric record. By implementing a hierarchical calibration pipeline to harmonise Ozone Monitoring Instrument (OMI) and Tropospheric Monitoring Instrument (TROPOMI) observations, the study effectively eliminated a 6.61-fold systematic instrument offset, producing a 21-year time series (2004–2025) of tropospheric NO₂ concentrations. Simultaneously, a 24-year analysis (2000–2024) of coastline dynamics was conducted using the Landsat archive to quantify land area changes across the island and within a 1.03 km² Archaeological Area of Interest (AOI). Results indicate that atmospheric NO₂ concentrations stabilised following a 2015 peak, while coastal erosion represents a measurable risk to structural integrity. Net land loss of 18,400 m² was documented within the AOI, driven by localised geomorphological factors and exposure to Meltemi winds. The results indicate that these environmental processes are physically independent yet collectively require a multilayered conservation strategy to protect vulnerable archaeological heritage from atmospheric pollution and coastal retreat. Furthermore, the research highlights the value of long-term satellite datasets spanning more than two decades for supporting heritage monitoring and management, especially in remote or hard-to-reach locations. Through the analysis of the spatial and temporal characteristics of these sensors, the research enables the identification of hazard proxies that can inform risk-aware decision-making. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

Seasonal ice is typically present in the southern Gulf of Saint Lawrence from December through March; however, climate change is predicted to reduce this season and alter local ecosystems, geomorphologies, and infrastructure. This impact assessment ascertains the influence of climate change on the ice coverage along Prince Edward Island’s coast. Ice concentration data from 50 study sites were logarithmically correlated with cumulative freezing degree days (FDDs). Correlations were generally good (mean R² = 0.63), although poorer values were observed in areas with greater exposure to wind and waves. An ensemble of the CMIP6 models’ forecasts of future temperatures showed that FDD will drop from an average of 487 °C days during the historical period (1981–2025) to less than 164 °C days in the 2090s under a low-emission scenario, SSP1-2.6. For the same study period, a high-emission scenario (SSP5-8.5) projects FDD to drop to 28 °C days by the end of the century, while a moderate-emission scenario (SSP2-4.5) forecasts 97 °C days annually. Seasonal ice indices demonstrated a similarly substantial decrease, from an average historical value of 11.1 to 3.8, 3.2, and 0.8 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The length of the ice season was also analyzed, with mean season lengths for the 2090s ranging from 3 to 24 days, depending on the emission scenario, representing a 70–96% reduction in season length from the baseline observation. Mild variations were measured in the rate of ice loss throughout the province; however, significant differences in the ice coverage’s baseline values, due to local currents and wave exposure, led to a broad range in the relative proportions of ice loss, with areas along the eastern coastline projecting zero ice winters. Over the next 80 years, projections point to a considerable decline in ice coverage around Prince Edward Island. Full article

The Monterrey Metropolitan Area (MMA), the largest urban and industrial center in northeastern Mexico, faces increasing groundwater stress driven by rapid urban expansion, recurrent drought, and limited surface-water availability. Since 2024, the San Juan River has been considered a potential source of treaty water under the 1944 U.S.–Mexico Water Treaty, further intensifying pressure on regional water resources. This study evaluates changes in groundwater recharge potential between 1990 and 2022 using an integrated Remote Sensing–Geographic Information System framework combined with the Analytic Hierarchy Process. Eight thematic layers—geology, structural lineaments, slope, geomorphology, precipitation, drainage density, Normalized Difference Vegetation Index, and soil type—were weighted to derive a Groundwater Potential Index and delineate recharge zones. Results show a pronounced redistribution of recharge capacity over 32 years. Very low recharge areas increased by 1021.3 km², while very high recharge zones decreased by 100.4 km². In total, more than 1100 km² experienced degradation in recharge potential, mainly associated with urban growth and land-use change. These findings highlight the urgent need for sustainable groundwater management, stronger land-use planning, and protection of recharge areas. Coordinated action among stakeholders and robust regulatory enforcement will be essential as the region navigates future growth and international water obligations. Full article

Land-use and land-cover change plays a critical role in shaping urban expansion patterns and modifying near-surface soil conditions, hydrological behaviour, and geomorphological stability in rapidly developing regions. This study presents a comparative modelling framework to analyze long-term land-use change and its implications for regional-scale geotechnical risk screening by integrating historical land-use classification, Markov transition analysis, and machine learning–based spatial simulation. Landsat imagery from 1985 and 2024 was classified using a Support Vector Machine approach, and future land-use projections for 2063 were generated using both the TerrSet Land Change Modeler (LCM) and the GeoFLUS model under identical transition demands. Spatial driving variables included topographic, hydrological, and accessibility-related factors that influence soil behaviour and urban suitability. The results reveal sustained urban expansion primarily driven by the systematic conversion of agricultural land into built-up surfaces, while forested areas and water bodies exhibit high class persistence, as indicated by dominant diagonal values in the Markov transition matrix. Although both models reproduce consistent directional trends, they generate distinct spatial allocation patterns, with LCM producing compact and centralized growth and GeoFLUS generating more spatially dispersed expansion. These differences lead to contrasting implications for potential settlement, flooding, and slope instability zones. By treating future land-use maps as alternative geotechnical screening scenarios rather than fixed predictions, this study demonstrates how model uncertainty can be incorporated into hazard-sensitive planning. The proposed framework supports preliminary geotechnical zoning and infrastructure planning by identifying robust development corridors and spatial uncertainty zones where detailed site investigations may be prioritized. The methodology is transferable to other rapidly urbanizing regions facing complex soil and geomorphological constraints. Full article

Viticulture is a vital sector of agriculture and economy exhibiting susceptibility to climate change, particularly in the Mediterranean regions. The present investigation examines the climatic suitability for vineyards development in Greece by exploiting geomorphological and bioclimatic data for the reference climatic period 1970–2000. The data is sourced from the ERA5-Land dataset and analyzed with R. The objective is to create a specific crop suitability map based on a simple, transparent model implemented through coding. This map identifies the climatically suitable areas for grapevine cultivation during the reference period. Results demonstrate that the model is highly adaptable, as both variable thresholds and areas of interest can be modified, while incorporating future climate scenarios can be performed, allowing for dynamic reconfiguration. According to the mapped climatic suitability, 55.1% of Greece is rated 3.5–4.0, and 12.9% is rated 4.0–4.5. The total suitability over Greece is calculated with a score of 3.5–4.0 for the 50.9% of total area, and for a score of 4.0–4.5, the covered area is 12.9%. Considering the Corine Land Cover classification as the reference land cover dataset, the false-negative areas (the model indicates that an area with vines is not suitable) are only 1.5% of the areas defined as viticultural. By providing clear and accurate spatial information, the model supports informed decision-making and the development of adaptation strategies, enhancing, therefore, the resilience and sustainability of viticulture in the context of climate change. Full article

Nowadays, river systems exhibit significant geomorphic changes that primarily reflect their response to the climate signal, driven by ongoing climate change. In this context, detecting future trends in riverbed dynamics is crucial, especially from a river management perspective. The purpose of the study is to identify long-term trends in riverbed evolution at the Băleni gauging station on the Ialomița River, based on the channel-forming discharge concept, through the end of the 21st century. To achieve this, a comprehensive methodology was developed that primarily focuses on calculating the effective discharge (Qe) as a key driver of riverbed dynamics, using discharges simulated by the E–HYPE hydrological model forced by eight EURO–CORDEX EUR–11 ensemble climate projections under the RCP 4.5 and RCP 8.5 scenarios up to 2100. The results of the study indicate Qe values ranging between 7.49 m³/s and 12.79 m³/s for RCP 4.5, and between 5.66 m³/s and 13.94 m³/s for RCP 8.5. Based on the ensemble mean of Qe, different riverbed evolution trends and are identified: a state of dynamic equilibrium under RCP 4.5, suggesting that the riverbed is probable to maintain its geomorphological state similar to the present; and pronounced variability under RCP 8.5, indicating intense erosion processes until mid-century, followed by a slight aggradation trend that may intensify at the end of the century, with Qe being 23.27% lower than the reference period. Overall, the Qe_8.5 evolution suggests a potential future alteration of the Ialomița riverbed. Beyond its main findings, this study provides a methodological framework for assessing future effective discharge and may support river management and restoration planning in the study area. Full article

A typical Tamarix nebkha was studied in the southern Qaidam Basin, China. K-feldspar pIRIR dating was applied to establish a reliable chronological framework, and an Undatable age–depth model was constructed. Accumulation rates (AR) declined in stages: rapid (~1.33 cm/a; ~370–260 yr BP), slower (~0.75 cm/a; ~260–130 yr BP), and slowest (~0.31 cm/a; ~130 yr BP-present). This dynamic pattern is likely influenced by a combination of regional aeolian activity variations, geomorphological evolution, and the intrinsic growth dynamics of the nebkha itself. To further understand the relationship between nebkha development and climatic conditions, a δ¹³C sequence was reconstructed using Tamarix plant remains preserved within the sediments. Based on shifts in leaf-level δ¹³C values, which indicate changes in water use efficiency, water availability over the past 370 years was inferred and divided into three main phases: relatively sufficient from 1650 to 1690, gradually decreasing during 1690–1870, and increasing again after 1870. The δ¹³C trend closely correlates with temperature variations derived from δ¹⁸O records of the Malan ice core. This suggests that in this hyper-arid region, the development of Tamarix nebkhas is primarily controlled by glacial meltwater and snowmelt runoff from the Kunlun Mountains, rather than by local precipitation. Full article

Background: Juniperus macrocarpa Sm. is a key shrub species of the Mediterranean coastal dune systems. The species, considered vulnerable, often shows fragmented or declining populations due to coastal erosion and human pressure. However, along a protected stretch of the northern Tuscany coast it displays an opposite trend, with an apparent expansion of the species. Methods: To assess recent population dynamics, we compared high-resolution aerial imagery from 2013 with UAV orthophotos from 2023 across two dune systems of the Migliarino–San Rossore–Massaciuccoli Regional Park (Italy). The dune profile was divided into three belts (B1: shifting dune; B2: consolidated grassland dune; B3: consolidated juniper dune). A total of 368 plots (10 × 10 m) were analyzed to quantify temporal changes in individual abundance and vegetation cover. Results: Over the ten-year period, total abundance increased from 99 to 342 individuals (+245%) at Lecciona and from 117 to 324 individuals (+177%) at Marina di Vecchiano. Mean cover per plot increased significantly at both sites (overall p < 0.001), with the strongest proportional increases recorded in the seaward belts (B1: up to +1220% in abundance and +4500% in cover) revealing a clear shift from an inner-dune concentration in 2013 to a more homogeneous spatial distribution across the entire dune system in 2023. Conclusions: Under conditions of low anthropogenic disturbance, shoreline stability, or geomorphological progradation, J. macrocarpa is able to expand well beyond its recognized ecological niche. These findings demonstrate the central role of geomorphological and disturbance regimes in driving coastal dune vegetation dynamics and highlight the need for adaptive, site-specific management strategies for the long-term conservation of priority habitat 2250/EUNIS N1B. Full article

Geomorphological change is a fundamental consequence of high-magnitude flood events, as extreme hydraulic forcing can rapidly reshape river channels, redistribute sediment, and alter floodplain connectivity. This study applies multi-temporal UAS-based Structure-from-Motion (SfM) photogrammetry to quantify flood-induced geomorphological changes along two representative reaches of the Lilas River (Evia Island, Central Greece) affected by the extreme August 2020 flash flood. High-resolution aerial surveys were conducted prior to the event (June 2018) and shortly after the flood (September 2020), producing Digital Surface Models (DSMs) and orthomosaics with a ground sampling distance of ~2.5 cm. Differential DSM analysis reveals pronounced spatial heterogeneity in erosion and deposition, with net erosional lowering locally exceeding 7 m and depositional aggradation reaching up to ~5 m after accounting for vegetation effects. Channel widening was the dominant response, with cross-sectional widths increasing by a factor of three to nine at selected locations, driven primarily by lateral bank erosion. The results highlight the strong interaction between extreme hydrological forcing, loose alluvial sediments, vegetation removal, and human interventions such as roads and engineered terraces. The study demonstrates how repeatable UAS–SfM workflows can provide quantitative evidence to support post-flood assessment, guide infrastructure adaptation, and inform river restoration and flood risk management in Mediterranean catchments prone to extreme events. Full article

Freshwater fish habitat simulation is a vital technology for assessing the state and dynamics of aquatic ecosystems under changing environments. Based on a comprehensive dataset spanning 1991–2024, this study constructs a domain knowledge map by integrating co-citation analysis, keyword burst detection, and social network metrics. The bibliometric results quantitatively identify leading contributors and trace the field’s exponential growth. Complementing this, a critical technical review reveals a significant paradigm shift in modeling methodologies: moving from traditional univariate suitability curves to advanced multivariate and artificial intelligence (AI)-based frameworks. Despite these advancements, our analysis highlights critical gaps in addressing habitat connectivity and broad environmental stressors. To overcome these limitations, we propose a novel framework that integrates landscape pattern indices with circuit theory to quantify habitat patch arrangement and ecological flows. Furthermore, we advocate for future research to explicitly incorporate climate change scenarios (e.g., thermal regime shifts) and geomorphological processes. This study offers both a macroscopic overview of the discipline’s evolution and a roadmap for developing robust, ecosystem-based management tools. Full article

This study is essential for medium- and long-term land-use management, as land-use patterns directly influence local economic and social development. Geographic Information System (GIS) techniques are fundamental tools for analyzing a wide range of geomorphological processes, including relief fragmentation density, relief energy, soil texture, slope gradient, and slope orientation. The present research focuses on the Pesceana river basin in the Southern Carpathians, Romania. It addresses three main objectives: (1) to analyze land-use dynamics derived from CORINE Land Cover (CLC) data between 1990 and 2018, along with the long-term distribution of the Normalized Difference Vegetation Index (NDVI) for the period 2000–2025; (2) to evaluate the basin’s natural potential byintegrating topographic data (contour lines and profiles) with relief fragmentation density, relief energy, vegetation cover, soil texture, slope gradient, aspect, the Stream Power Index (SPI), and the Topographic Wetness Index (TWI); and (3) to assess the spatial distribution of habitat types, characteristic plant associations, and soil properties obtained through field investigations. For the first two research objectives, ArcGIS v. 10.7.2 served as the main tool for geospatial processing. For the third, field data were essential for geolocating soil samples and defining vegetation types across the entire 247 km² area. The spatiotemporal analysis from 1990 to 2018 reveals a landscape in which deciduous forests clearly dominate; they expanded from an initial area of 80 km² in 1990 to over 90 km² in 2012–2018. This increase, together with agricultural expansion, is reflected in the NDVI values after 2000, which show a sharp increase in vegetation density. Interestingly, other categories—such as water bodies, natural grasslands, and industrial areas—barely changed, each consistently representing less than 1 km² throughout the study period. These findings emphasize the importance of land-use/land-cover (LULC) data within the applied GIS model, which enhances the spatial characterization of geomorphological processes—such as vegetation distribution, soil texture, slope morphology, and relief fragmentation density. This integration allows a realistic assessment of the physical–geographic, landscape, and pedological conditions of the river basin. Full article

This study presents a comprehensive geospatial framework for assessing coastal vulnerability and ecosystem service distribution along the Greek coastline, one of the longest and most diverse in Europe. The framework integrates two complementary components: a Coastal Erosion Vulnerability Index applied to all identified beach units, and Coastal Flood Risk Indexes focused on low-lying and urbanized coastal segments. Both indices draw on harmonized, open-access European datasets to represent environmental, geomorphological, and socio-economic dimensions of risk. The Coastal Erosion Vulnerability Index is developed through a multi-criteria approach that combines indicators of physical erodibility, such as historical shoreline retreat, projected erosion under climate change, offshore wave power, and the cover of seagrass meadows, with socio-economic exposure metrics, including land use composition, population density, and beach-based recreational values. Inclusive accessibility for wheelchair users is also integrated to highlight equity-relevant aspects of coastal services. The Coastal Flood Risk Indexes identify flood-prone areas by simulating inundation through a novel point-based, computationally efficient geospatial method, which propagates water inland from coastal entry points using Extreme Sea Level (ESL) projections for future scenarios, overcoming the limitations of static ‘bathtub’ approaches. Together, the indices offer a spatially explicit, scalable framework to inform coastal zone management, climate adaptation planning, and the prioritization of nature-based solutions. By integrating vulnerability mapping with ecosystem service valuation, the framework supports evidence-based decision-making while aligning with key European policy goals for resilience and sustainable coastal development. Full article