Search Results (312)

The present paper reports for the first time the comparison of radar-derived eddy dissipation rate (EDR) and vertical velocity with measurements from six aircraft for an intense squall line crossing Hong Kong. The study objectives are three-fold: (i) to characterise the structural dynamics of the intense squall line; (ii) to identify the dynamical change in EDR and vertical velocity during its eastward propagation across Hong Kong with a view to gaining insight into the intensity change of the squall line and the severity of its impact on aircraft flying near it; (iii) to carry out quantitative comparison of EDR and vertical velocity derived from remote sensing instruments, i.e., weather radars and in situ measurements from aircraft, so that the quality of the former dataset can be evaluated by the latter. During the passage of the squall line and taking reference of the radar reflectivity, vertical circulation and the subsiding flow at the rear, it appeared to be weakening in crossing over Hong Kong, possibly due to land friction by terrain and urban morphology. This is also consistent with the maximum gusts recorded by the dense network of ground-based anemometers in Hong Kong. However, from the EDR and the vertical velocity of the aircraft, the weakening trend was not very apparent, and rather severe turbulence was still recorded by the aircraft flying through the squall line into the region with stratiform precipitation when the latter reached the eastern coast of Hong Kong. In general, the radar-based and the aircraft-based EDRs are consistent with each other. The radar-retrieved maximum vertical velocity may be smaller in magnitude at times, possibly arising from the limited spatial and temporal resolutions of the aircraft data. The results of this paper could be a useful reference for the development of radar-based turbulence products for aviation applications. Full article

Urban coral reefs in Jakarta Bay and the Thousand Islands, Indonesia, are chronically exposed to land-based pollution and increasing thermal stress. These reefs—including the site of Indonesia’s first recorded coral bleaching event in 1983—remain highly vulnerable to climate-induced disturbances. During the fourth global coral bleaching event (GCBE), we recorded selective bleaching in the region, associated with a Degree Heating Weeks (DHW) value of 4.8 °C-weeks. Surveys conducted in January 2024 across a shelf gradient at four representative islands revealed patchy bleaching, affecting various taxa at depths ranging from 3 to 13 m. A follow-up survey in May 2024, which tracked the fate of 42 tagged bleached colonies, found that 36% had fully recovered, 26% showed partial recovery, and 38% had died. Bleaching responses varied across taxa, depths, and microhabitats, often occurring in close proximity to unaffected colonies. While some corals demonstrated resilience, the overall findings underscore the continued vulnerability of urban reefs to escalating thermal stress. This highlights the urgent need for a comprehensive and coordinated national strategy—not only to monitor bleaching and assess reef responses, but also to strengthen protection measures and implement best-practice restoration. Such efforts are increasingly critical in the face of more frequent and severe bleaching events projected under future climate scenarios. Full article

Heatwaves intensified by climate change increasingly threaten urban populations, especially the elderly. However, most existing studies have concentrated on short-term or single-scale analyses, lacking a comprehensive understanding of how land cover changes and urbanization affect the vulnerability of the elderly to extreme heat. This study aims to investigate the spatiotemporal distribution patterns of heat-related health risks among the elderly in Nanchang City and to identify their key driving factors within the context of rapid urbanization. This study employs Crichton’s risk triangle framework to the heat-related health risks for the elderly in Nanchang, China, from 2002 to 2020 by integrating meteorological records, land surface temperature, land cover data, and socioeconomic indicators. The model captures the spatiotemporal dynamics of heat hazards, exposure, and vulnerability and identifies the key drivers shaping these patterns. The results show that the heat health risk index has increased significantly over time, with notably higher levels in the urban core compared to those in suburban areas. A 1% rise in impervious surface area corresponds to a 0.31–1.19 increase in the risk index, while a 1% increase in green space leads to a 0.21–1.39 reduction. Vulnerability is particularly high in economically disadvantaged, medically under-served peripheral zones. These findings highlight the need to optimize the spatial distribution of urban green space and control the expansion of impervious surfaces to mitigate urban heat risks. In high-vulnerability areas, improving infrastructure, expanding medical resources, and establishing targeted heat health monitoring and early warning systems are essential to protecting elderly populations. Overall, this study provides a comprehensive framework for assessing urban heat health risks and offers actionable insights into enhancing climate resilience and health risk management in rapidly urbanizing regions. Full article

Traffic crashes represent a major challenge for road safety, public health, and mobility management in complex urban environments, particularly in metropolitan areas characterized by intense traffic flows, high population density, and strong commuter dynamics. The development of short-term traffic crash prediction models represents a fundamental line of analysis in road safety research within the scientific community. Among these efforts, macro-level modeling plays a key role by enabling the analysis of the spatiotemporal relationships between diverse factors at an aggregated zonal scale. However, in cities like Bogotá, predicting short-term traffic crashes remains challenging due to the complexity of these spatiotemporal dynamics, underscoring the need for models that more effectively integrate spatial and temporal data. This paper presents a strategy based on deep learning techniques to predict short-term spatiotemporal traffic crashes in Bogotá using 2019 data on socioeconomic, land use, mobility, weather, lighting, and crash records across TMAU and TAZ zones. The results showed that the strategy performed with a model called SpatioConvGru-Net with top performance at the TMAU level, achieving $R^2$ = 0.983, MSE = 0.017, and MAPE = 5.5%. Its hybrid design captured spatiotemporal patterns better than CNN, LSTM, and others. Performance improved at the TAZ level using transfer learning. Full article

Urban and regional ecosystems are increasingly challenged by the compounded effects of climate change and intensive land use. In this study, a predictive assessment framework for ecosystem resilience in the Beijing–Tianjin–Hebei region was developed by integrating multi-source remote sensing data, with the aim of quantitatively evaluating the coupled effects of climate change and land use change on future ecosystem resilience. In the first stage of the study, the SD-PLUS coupled modeling framework was employed to simulate land use patterns for the years 2030 and 2060 under three representative combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Building upon these simulations, ecosystem resilience was comprehensively evaluated and predicted on the basis of three key attributes: resistance, adaptability, and recovery. This enabled a quantitative investigation of the spatio-temporal dynamics of ecosystem resilience under each scenario. The results reveal the following: (1) Temporally, ecosystem resilience exhibited a staged pattern of change. From 2020 to 2030, an increasing trend was observed only under the SSP1-2.6 scenario, whereas, from 2030 to 2060, resilience generally increased in all scenarios. (2) In terms of scenario comparison, ecosystem resilience typically followed a gradient pattern of SSP1-2.6 > SSP2-4.5 > SSP5-8.5. However, in 2060, a notable reversal occurred, with the highest resilience recorded under the SSP5-8.5 scenario. (3) Spatially, areas with high ecosystem resilience were primarily distributed in mountainous regions, while the southeastern plains and coastal zones consistently exhibited lower resilience levels. The results indicate that climate and land use changes jointly influence ecosystem resilience. Rainfall and temperature, as key climate drivers, not only affect land use dynamics but also play a crucial role in regulating ecosystem services and ecological processes. Under extreme scenarios such as SSP5-8.5, these factors may trigger nonlinear responses in ecosystem resilience. Meanwhile, land use restructuring further shapes resilience patterns by altering landscape configurations and recovery mechanisms. Our findings highlight the role of climate and land use in reshaping ecological structure, function, and services. This study offers scientific support for assessing and managing regional ecosystem resilience and informs adaptive urban governance in the face of future climate and land use uncertainty, promotes the sustainable development of ecosystems, and expands the applicability of remote sensing in dynamic ecological monitoring and predictive analysis. Full article

Cropland is a critical resource for safeguarding food security. Ensuring both the quantity and quality of cropland is essential for achieving zero hunger and promoting sustainable agriculture. However, whether urbanization-induced cropland loss poses a substantial threat to regional food security remains a key concern. This study examines the central region of the Yangtze River Delta (YRD) in China, integrating CLCD (China Land Cover Dataset) land use/cover data (2001–2023), MOD17A2H net primary productivity (NPP) data, and statistical records to evaluate the impacts of urban expansion on grain yield. The analysis focuses on three components: (1) grain yield loss due to cropland conversion, (2) compensatory yield from newly added cropland under the requisition–compensation policy, (3) yield increases from stable cropland driven by agricultural enhancement strategies. Using Sen’s slope analysis, the Mann–Kendall trend test, and hot/coldspot analysis, we revealed that urban expansion converted approximately 14,598 km² of cropland, leading to a grain production loss of around 3.49 million tons, primarily in the economically developed cities of Yancheng, Nantong, Suzhou, and Shanghai. Meanwhile, 8278 km² of new cropland was added through land reclamation, contributing only 1.43 million tons of grain—offsetting just 41% of the loss. In contrast, stable cropland (102,188 km²) contributed an increase of approximately 9.84 million tons, largely attributed to policy-driven productivity gains in areas such as Chuzhou, Hefei, and Ma’anshan. These findings suggest that while compensatory cropland alone is insufficient to mitigate the food security risks from urbanization, the combined strategy of “Safeguarding Grain in the Land and in Technology” can more than compensate for production losses. This study underscores the importance of optimizing land use policy, strengthening technological interventions, and promoting high-efficiency land management. It provides both theoretical insight and policy guidance for balancing urban development with regional food security and sustainable land use governance. Full article

As a crucial manifestation of ecosystem water regulation and supply functions, water conservation plays a vital role in regional ecosystem development and sustainable water resource management. This study investigates nine major Chinese river basins (Songliao, Haihe, Huaihe, Yellow, Yangtze, Pearl, Southeast Rivers, Southwest Rivers, and Inland Rivers) through integrated application of the InVEST model and geographical detector model. We systematically examine the spatiotemporal heterogeneity of water conservation capacity and its driving mechanisms from 1990 to 2020. The results reveal a distinct northwest–southeast spatial gradient in water conservation across China, with lower values predominating in northwestern regions. Minimum conservation values were recorded in the Inland River Basin (15.88 mm), Haihe River Basin (42.07 mm), and Yellow River Basin (43.55 mm), while maximum capacities occurred in the Pearl River Basin (483.68 mm) and Southeast Rivers Basin (517.21 mm). Temporal analysis showed interannual fluctuations, peaking in 2020 at 130.98 mm and reaching its lowest point in 2015 at 113.04 mm. Precipitation emerged as the dominant factor governing spatial patterns, with higher rainfall correlating strongly with enhanced conservation capacity. Land cover analysis revealed superior water retention in vegetated areas (forests, grasslands, and cultivated land) compared to urbanized and bare land surfaces. Our findings demonstrate that water conservation dynamics result from synergistic interactions among multiple factors rather than single-variable influences. Accordingly, we propose that future water resource policies adopt an integrated management approach addressing climate patterns, land use optimization, and socioeconomic factors to develop targeted conservation strategies. Full article

(1) Background: Urban parks play a critical role in conserving biodiversity within city landscapes, yet the effects of fine-scale microhabitat heterogeneity remain poorly understood. This study examines how land cover and vegetation unit type within parks influence butterfly diversity. (2) Methods: From July to September 2019 and June to September 2020, adult butterflies were surveyed in 27 urban parks across Beijing. We classified vegetation into units based on vertical structure and management intensity, and then applied the patch–matrix framework and landscape metrics to quantify fine-scale heterogeneity in vegetation unit composition and configuration. Generalized linear models (GLM), generalized additive models (GAM), and random forest (RF) models were applied to identify factors influencing butterfly richness (Chao1 index) and abundance. (3) Results: In total, 10,462 individuals representing 37 species, 28 genera, and five families were recorded. Model results revealed that the proportion of park area covered by spontaneous herbaceous areas (SHA), wooded spontaneous meadows (WSM), and the Shannon diversity index (SHDI) of vegetation units were positively associated with butterfly species richness. In contrast, butterfly abundance was primarily influenced by the proportion of park area covered by cultivated meadows (CM) and overall green-space coverage. (4) Conclusions: Fine-scale vegetation patch composition within urban parks significantly influences butterfly diversity. Our findings support applying the patch–matrix framework at intra-park scales and suggest that integrating spontaneous herbaceous zones—especially wooded spontaneous meadows—with managed flower-rich meadows will enhance butterfly diversity in urban parks. Full article

In recent years, the world has seen a significant increase in extreme weather events, such as floods, hurricanes, and storms, which have caused extensive damage to infrastructure and communities. These events result from natural phenomena and human-induced factors, including climate change and natural climate variations. For instance, the floods in Europe in 2024 and the hurricanes in the United States have highlighted the vulnerability of urban and rural areas. These extreme events are often unpredictable and pose considerable challenges for spatial planning and risk management. This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. By utilizing data such as weather records, land use and land cover (LULC) information, topographic LIDAR data, and advanced predictive models, the study aims to identify the most vulnerable areas and provide recommendations for risk mitigation. The results indicate that integrating these technologies can improve forecasting accuracy, thereby supporting more informed decisions in land management. Given the effects of climate change and the increasing frequency of extreme events, adopting advanced forecasting and planning tools is crucial for protecting communities and reducing economic and social damage. This method was applied to the Calopinace area, also known as the Calopinace River or Fiumara della Cartiera, which crosses Reggio Calabria and is notorious for its historical floods. It can serve as part of an early warning system, enabling alerts to be issued throughout the monitored area. Furthermore, it can be integrated into existing emergency protocols, thereby enhancing the effectiveness of disaster response. Future research could investigate incorporating additional data and AI techniques to improve accuracy. Full article

This study investigates the effect of high urban land temperatures on pesticide residue (PR) accumulation in cabbage and lettuce and on public health in the Greater Accra Metropolitan Area (GAMA) in Ghana. A comparative toxicological analysis regarding the food system was conducted with 66 farmers across three land surface temperatures: low (Atomic, n = 22), moderate (Ashaiman, n = 22), and high (Korle-Bu, n = 22). Pesticide residue concentrations were assessed using an ANOVA to examine spatial variations across sites. The results indicate a strong correlation between high land surface temperatures and pesticide residue accumulation, with lettuce recording significantly (p < 0.05) higher PR levels than cabbage. Several pesticides, including carbendazim (CBZ), Imidacloprid (IMI), Thiamethoxam (TMX), and Chlorpyrifos (CHL), exceeded the maximum residue limits (MRLs) set by the World Health Organization (WHO) and the European Union (EU) at moderate and high-temperature sites. carbendazim was the dominant pesticide detected, with a concentration of 19.0 mg/kg in lettuce, which far exceeded its maximum residue limit (MRL) of 0.10 mg/kg across all study sites. Statistical analyses (PERMANOVA) confirmed that land surface temperatures and pesticide types significantly influenced the PR concentrations. Public health risk assessments indicate that children are more vulnerable to pesticide exposure than adults. The toxicity hazard quotient (THQ) for organophosphate pesticides, particularly CHL and Dimethoate (DMT), exceeded safe thresholds at moderate and high-temperature sites. Full article

The effects of climate change and urbanisation, such as more intense rainfall and changing land use patterns, are putting increasing pressure on urban drainage systems. This study proposes a comprehensive methodology for evaluating the effectiveness of sustainable urban drainage systems (SUDSs) in mitigating flooding and managing stormwater in both current and future scenarios. The approach integrates geospatial data, including digital elevation models (DEMs) and land use information, to delineate catchments and characterise hydrological parameters. Historical rainfall records and hydrological modelling were employed to define two baseline storm events: an extreme storm involving 422 mm of rainfall over 2 h, and an average storm involving 2.84 mm of rainfall over 1 h and 18 min. Future scenarios were developed by updating these baseline events using annual rates of change in maximum and average precipitation derived from climate projections between 2025 and 2100. The analysis incorporates seven CMIP6 climate scenarios: SSP1-1.9, SSP1-2.6, SSP4-3.4, SSP4-2.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5. A stochastic simulation of 1000 storms per year was carried out using a custom-built conceptual hydrological model based on CN and developed in Python, which reflects interannual variability. The results show that extreme storm volumes could increase by up to seven times and average storm volumes by up to two and a half times. Additionally, discharge peaks could exceed baseline values by up to 20% in some years, suggesting an increased occurrence of extreme runoff events. The methodology assesses SUDS performance by comparing runoff and hydrological responses between baseline and future estimates. This framework enables vulnerabilities and adaptation needs to be identified, ensuring the long-term effectiveness of SUDSs in managing urban flood risk. Addressing uncertainties in climate and land use projections emphasises the importance of integrating SUDS assessments into wider urban resilience strategies. Full article

Land use composition, water level fluctuations (WLFs), and biogeographical factors are recognized as key drivers of phytoplankton dynamics in reservoir ecosystems. This two-year study presents the first assessment of the combined effects of catchment land use, WLFs, and geographical distance on phytoplankton biomass and community composition across twelve Mediterranean reservoirs in Cyprus, which serve primarily for drinking water supply and irrigation. The results show that higher phytoplankton biomass was recorded in reservoirs whose catchments had >30% coverage by developed land (urban and agricultural), suggesting that increased anthropogenic pressures may lead to nutrient enrichment and elevated productivity. However, despite elevated biomass, no consistent spatial patterns were observed in phytoplankton community composition. The geographical distance between reservoirs had only a minor effect on species distribution, implying that other factors—such as water residence time or hydrological variability—play a more prominent role in shaping community structure. Phytoplankton biomass maxima were most often recorded during periods of elevated water levels and were typically dominated by Chlorophyta, Dinoflagellata, Bacillariophyta, and Charophyta. The pronounced temporal variability in species composition across all reservoirs points to a highly dynamic system, where environmental fluctuations strongly influence community assembly. This study provides the first comprehensive data on phytoplankton in Cyprus reservoirs, highlighting the importance of land use and hydrological regulation for water quality management in similar settings. Importantly, this baseline dataset can support the implementation of the Water Framework Directive (WFD) by contributing to the definition of ecological status classes, establishing reference conditions, and guiding future monitoring and assessment efforts. Expanding such datasets through coordinated, basin-wide monitoring initiatives is essential to improve our understanding of phytoplankton dynamics and their role in ecosystem functioning under the pressures of climate change and intensified land use in this Mediterranean “hot spot”. Full article

Understanding spatiotemporal mismatches between urban expansion and population dynamics is essential for guiding sustainable development in rapidly urbanizing regions. Using multi-source nighttime light (NTL) images and global settlement layers, this study investigates the settlement growth pattern and potential spatiotemporal mismatch with population distribution in Guangdong, China, from 1995 to 2019 at a 5-year interval. Specifically, population spatialization in urban and rural areas is separately mapped by adopting a population-based thresholding method, achieving strong agreement with the census record. Our analysis reveals distinct expansion patterns and mismatch conditions across Guangdong’s Core, Belt, and District subzones. The Core and District subzones primarily experienced infilling and edge-expansion urban growth, while the Belt subzone exhibited more dispersed spatial patterns. Notably, only 5 of 21 prefectures exhibited faster population growth than urban expansion, likely due to sustained migration driven by economic opportunities and advanced urbanization. Quantitatively, both urban expansion and population growth followed a Core, Belt, District order. Spatially, population-dominated areas were primarily clustered within 10 km of urban centers, while the District subzone extensively displayed overfilled settlements, indicating low-efficient land use. Temporally, urban growth relative to population in the Core subzone turned from slower pre-2000 to faster post-2000, followed by gradual deceleration, while the Belt subzone maintained balanced growth throughout the study period. The District subzone sustained faster urban growth from 2000 to 2019. Findings of the study provide an important reference for scientific urban planning and sustainable regional development, not only in Guangzhou but other rapidly urbanizing regions globally. Full article

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

Land subsidence significantly threatens urban infrastructure, agricultural productivity, and environmental sustainability. This study develops a land subsidence susceptibility model by integrating Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) data with key geospatial factors using machine learning approaches. The study focuses on the Attica prefecture, Greece, and utilizes SBAS InSAR data from 2015 to 2021 to extract ground deformation velocities by classifying them into four susceptibility levels: stable, low, moderate, and high. The susceptibility results indicate that stable zones constitute 58.2% of the study area, followed by low (27.2%), moderate (11.2%), and high susceptibility zones (3.4%), predominantly concentrated in areas undergoing hydrological stress and urbanization. Random Forest (RF) and XGBoost (XGB) models incorporate a comprehensive set of causal factors, including slope, aspect, land use, groundwater level, geology, and rainfall. The evaluation of the models includes accuracy metrics and confusion matrices. The XGB model achieved the highest performance, recording an accuracy of 94%, with well-balanced predictions across all susceptibility classes. Addressing class imbalance during model training improved the recall of minority classes, though with slight trade-offs in precision. Feature importance analysis identifies proximity to streams, land use, aspect, rainfall, and groundwater extraction as the most influential factors driving subsidence susceptibility. This methodology demonstrates high reliability and robustness in predicting land subsidence susceptibility, providing critical insights for land-use planning and mitigation strategies. These findings establish a scalable framework for regional and global applications, contributing to sustainable land management and risk reduction efforts. Full article