Search Results (2,051)

Rain-induced shallow landslides persistently disrupt Nepal’s mountain roads, frequently leading to fatalities, transport disruptions, and economic losses. This study develops physically validated, site-specific rainfall thresholds for the landslide-prone Kanti National Roadway (H37) by integrating empirical intensity–duration (I-D) analysis, antecedent rainfall metrics, and satellite-derived soil moisture data. Using 35 years of rainfall records (1990–2024) and 59 field-verified landslides (2017–2024), we derived a localized I-D threshold: I = 19.37 × D^−0.6215 (I: rainfall intensity in mm/h; D: duration in hours), effective for durations of 48–308 h, encompassing short intense storms and prolonged moderate rainfall. The Cumulative Antecedent Rainfall (CAR) method associated most failures with 3-day totals, while the Antecedent Precipitation Index (API) showed superior performance, with a 10-day threshold of 77 mm capturing all events. For physical validation, NASA’s SMAP Level-4 root-zone (0–100 cm) soil moisture data revealed a 1-day lag in response to rainfall; after adjustment, trends matched API saturation predictions and identified an inverse rainfall–moisture pattern before the 11 August 2019 landslide, indicating a potential instability precursor. This integration enhances predictive accuracy, bolsters mechanistic understanding of landslide hazards, and offers a scalable, cost-effective early-warning framework for data-scarce mountain regions, aiding climate-resilient infrastructure in regions with intensifying rainfall extremes. Full article

Narrow-range endemic plant species are highly sensitive to environmental variability due to their restricted distributions and narrow ecological niches, yet quantitative assessments of such species in Central Asian mountain ecosystem remain limited. This study applied an ensemble species distribution modeling (SDM) approach to assess the ecological constraints and conservation efforts of Lepidium olgae, a strict endemic species of the Nuratau Mountains in Uzbekistan. Species occurrence records from field surveys and herbarium data were integrated with remotely sensed climatic, vegetation, topographic, soil, and atmospheric variables. Parsimonious models (Generalized Linear Model (GLM), Maximum Entropy (MaxEnt), Multiple Adaptive Regression Splines (MARS), Surface Range Envelope (SRE)) were implemented in BIOMOD2 4.3.4, and ensemble predictions were used to reduce algorithmic uncertainty and identify core habitat patterns. Results showed that wet-season precipitation was the dominant driver of species distribution, followed by vegetation productivity (NDVI) and thermal stability, indicating a strong dependence on moisture availability and stable microhabitats. Ensemble projections revealed a highly fragmented potential distribution, with suitable habitats covering only 8% of the reserve area, closely matching the observed distribution of 6.5%. This strong spatial overlap confirms a narrowly constrained realized ecological niche. These findings highlight the critical role of microhabitat stability for the persistence of Lepidium olgae and provide a spatially explicit basis for prioritizing in situ conservation and guiding model informed translocation efforts. Full article

Interannual variability of sea level anomalies (SLA) in the South China Sea (SCS) is significantly influenced by large-scale climate modes; however, their temporal evolution and interdecadal modulation mechanisms remain insufficiently understood. Based on observational records and ERA5 reanalysis data spanning 1980–2022, this study employs a Bayesian Dynamic Linear Model (DLM) to quantify the time-varying impacts of El Niño-Southern Oscillation (ENSO) on interannual SLA variability across different subregions of the SCS and further investigates the modulation effect of the Pacific Decadal Oscillation (PDO) background state. The results indicate that ENSO is a key climatic driver of interannual SLA variability in the SCS; nevertheless, its influence exhibits pronounced non-stationarity, with dynamic regression coefficients showing clear phase-dependent fluctuations throughout the study period. The northern and eastern subregions display stronger responses to ENSO forcing, whereas the southern and western subregions exhibit relatively weaker signals. The negative phase of the PDO enhances the ENSO-SLA relationship, while the positive phase weakens it, with sign reversals occurring in certain subregions. Correlation analyses further suggest that ENSO influences SLA primarily through wind stress anomalies induced by sea level pressure (SLP) gradients, which regulate Ekman transport, whereas the PDO exerts an indirect effect mainly by modifying the large-scale background circulation structure. Full article

Large-scale urban tree donation campaigns are widely implemented worldwide as nature-based solutions for climate adaptation and mitigation; however, most programs lack individual-level traceability and post-donation monitoring, limiting accountability and evidence-based management. A fundamental prerequisite for longitudinal survival assessment is the existence of a reliable traceability infrastructure capable of linking individual trees to verified planting records over time. This study proposes and empirically evaluates a participatory digital traceability system that establishes this foundational infrastructure, conceptualized as a distributed data validation architecture for donation-based urban afforestation programs. The framework integrates (i) persistent digital identifiers, (ii) geospatial registration, (iii) distributed multi-stage validation, and (iv) structured citizen reporting, and is operationalized through an installation-free progressive web application (ArborizaCL). The approach was deployed in five real-world campaigns conducted in Valdivia, Chile (May–September 2025), registering 642 trees distributed to 240 participants. A total of 190 georeferenced planting reports were submitted, corresponding to an overall reporting rate of 29.6%. Reporting behavior varied substantially by institutional follow-up strategy: campaigns with active follow-up achieved a mean reporting rate of 54.0%, compared with 13.0% under passive strategies, yielding a 41.0 percentage point difference (315.8% relative increase). Spatial analysis of reported plantings showed a predominance of urban (51.1%) and peri-urban (42.1%) locations, enabling differentiated territorial assessment. These results indicate that while digital infrastructure enables traceability and transparent monitoring, sustained citizen engagement is strongly associated with institutional coordination mechanisms. Beyond environmental monitoring, the proposed framework contributes to information governance by demonstrating how participatory digital traceability systems can support distributed public-sector oversight and outcome-oriented evaluation. The framework provides a transferable methodological basis for strengthening monitoring capacity, transparency, and governance design in publicly funded afforestation initiatives and other distributed civic programs. Full article

Canine atopic dermatitis (CAD) is a chronic, pruritic inflammatory disease that affects up to 15% of the global canine population. Its etiopathogenesis is multifactorial, involving genetic, immunological, environmental, and dietary factors. It is characterized by pruritus, erythema, alopecia, and secondary lesions, predominantly affecting the abdomen, extremities, and ears. This retrospective cross-sectional descriptive study analyzed 735 medical records of dogs diagnosed with CAD treated at the Veterinary Specialty Center (CEVET) in Quito, Ecuador, between January 2018 and July 2025. Demographic, clinical, housing, diet, and cohabitation data were collected and statistically analyzed using χ² for qualitative variables and the Kruskal–Wallis test for quantitative variables, with post hoc analysis as appropriate. Additionally, pruritus severity was assessed using the Pruritus Visual Analog Scale (pVAS). A composite Clinical Severity and Distribution Score (CSDS) was also developed to classify disease severity. A multivariate logistic regression model was performed to identify factors associated with severe CAD. The results showed a predominance of CAD in adult dogs (84.2%) and purebred dogs (74.97%), with a slight majority being males (52.38%). Pruritus was the most frequent initial symptom (80.27%), with most cases presenting moderate-to-severe pruritus (pVAS 7–10). The most affected areas were the abdomen (24.49%) and forelimbs (17.68%). The geographical distribution showed a predominance of urban areas (88.84%) and cold climates (86.39%). Based on the CSDS, 53.2% of cases were classified as severe, 44.4% as moderate, and 2.4% as mild. Multivariate analysis revealed that grass exposure was significantly associated with severe CAD (OR = 1.78; 95% CI: 1.22–2.60; p = 0.003), while urban environment showed a non-significant trend toward increased severity (OR = 1.41; p = 0.071). Significant associations were identified involving sex and body weight, age and affected area, and temporal variations in the severity of pruritus, age group, and distribution of lesions. Among breeds, French Bulldogs, Standard Schnauzers, and Shih Tzus had the highest prevalence of CAD. These findings provide the first systematic epidemiological and clinical characterization of CAD in Ecuador, highlighting the role of environmental factors in disease severity and supporting the use of composite clinical scoring approaches in retrospective studies, thereby contributing to understanding of the disease and serving as a reference for early diagnosis, clinical management, and the development of preventive strategies. Full article

The concept of sustainability has evolved far beyond its initial environmental foundations, expanding into a multidimensional framework that integrates multinational policies, multicultural values, and multigenerational knowledge, but there is a paucity of bottom-up or grassroots research. This paper is a case history comprising oral history supported by rigorous documentation including military records, census data, genealogical records, and scholarship extending over four centuries. A more nuanced understanding of resilience and adaptation emerges. An analysis of recent scholarship indicates that sus-tainability is a dynamic, narrative-driven process that requires an in-depth understanding of the spatial and temporal consequences of global shifts, ranging from climate catastrophes to the global flows of capital and large migrations of people. This paper uses oral history to show the adaptation of a multinational, multicultural, multigenerational family in North America to the social, political, economic, and technological challenges faced over 400 years with a focus on sustainability and resilience. Full article

Urban mobility is a central challenge for sustainable and inclusive cities, as climate change, congestion, and spatial inequality increasingly reveal mobility patterns as expressions of deeper social and spatial structures. Inclusive urban mobility examines whether transport systems equitably support the everyday movements and accessibility needs of historically marginalized and underserved populations. The integration of artificial intelligence with geographic information science, combined with multimodal geospatial data fusion, provides powerful tools to diagnose and address these disparities by integrating heterogeneous data sources such as satellite imagery, GPS trajectories, transit records, volunteered geographic information, and social sensing data into scalable, high-resolution urban mobility analytics. This paper presents a systematic survey of recent GeoAI studies that fuse multiple geospatial data modalities for key urban mobility tasks, including accessibility mapping, demand forecasting, and origin–destination flow prediction, with particular emphasis on inclusive and equity-oriented applications. The review examines 18 multimodal GeoAI studies identified through a PRISMA-ScR screening process from 57 candidate publications between 2019 and 2025. The survey synthesizes methodological trends across data-, feature-, and decision-level fusion strategies, highlights the growing use of deep learning architectures, and examines emerging techniques such as knowledge graphs, federated learning, and explainable AI that support equity-relevant insights across diverse urban contexts. Building on this synthesis, the review identifies persistent gaps in population coverage, multimodal integration, equity optimization, explainability, validation, and governance, which currently constrain the inclusiveness and robustness of GeoAI applications in urban mobility research. To address these challenges, the paper proposes a structured research roadmap linking these gaps to concrete methodological and governance directions including equity-aware loss functions, adaptive multimodal fusion pipelines, participatory and human-in-the-loop workflows, and urban data trusts to better align multimodal GeoAI with the goals of inclusive, just, and sustainable urban mobility systems. Full article

The ongoing rise in temperatures due to climate change is one of the most critical considerations in the design of outdoor recreational spaces. Thermal conditions can affect people’s visitation patterns, satisfaction, health and well-being. In many developing countries, including Jordan, rapid urbanisation often occurs without sufficient planning for public outdoor spaces, thereby diminishing their quality. This study is the first to investigate the effects of temperature on visitor patterns and user satisfaction in Jordanian urban parks. A mixed-methods approach was employed, combining continuous measurements of outdoor temperature (Ta) and relative humidity (Rh) with a survey assessing users’ thermal sensation, satisfaction, and preferences across six urban parks in Amman, Jordan. Data were collected from 718 respondents in summer 2025. Visitation records for the surveyed parks were also obtained from local authorities for the monitored period. The results show that the mean Ta exceeded 30 °C in all surveyed parks during the monitoring period, with peak readings exceeding 41 °C. This resulted in a warm-to-hot thermal sensation among participants, with many preferring cooler conditions. A significant inverse relationship between temperature and park visitation rates (R² = 0.67, p = 0.001) was observed, with a 1 °C increase in outdoor temperature associated with approximately a 2.03 visitor decrease. Participants’ satisfaction was higher in parks with adequate amenities, such as shading, disability access, and green zones, than in parks with fewer amenities (p = 0.01, d = 0.63). The most reported areas for improvement included facilities, shaded seating areas, and perceived safety. The findings highlight the importance of considering outdoor thermal conditions when designing urban parks, as they shape public outdoor activity patterns, particularly in hot climates. Full article

Commercial Microwave Links (CMLs) have emerged as one of the most widely utilized opportunistic sensors for rainfall monitoring. However, rainfall retrieval using microwave links continues to face significant challenges in terms of accuracy, particularly for shorter path lengths. In recent years, a statistical approach has been demonstrated to effectively enhance retrieval accuracy. Concurrently, studies have shown that the selection of localized parameters can further optimize CML retrieval results. In this study, we evaluate and calibrate the probabilistic–statistical retrieval method proposed in a previous study for the Chinese region. Following their framework, we replace the global parameters with a Gamma rainfall distribution derived from local rain gauge observations, making the method more suitable for local climatic conditions. To validate the effectiveness of the improved method, we deployed three experimental microwave links with path lengths ranging from 420 m to 3.50 km and simultaneously recorded path attenuation along with rainfall data from surrounding rain gauges. The results show that the coefficient of determination and correlation coefficient between the proposed method and rain gauge observations reach 0.85 and 0.86, respectively, indicating a significant improvement over traditional models. The calibrated method performs particularly well during high-intensity rainfall events, demonstrating the importance of parameter localization for improving retrieval accuracy. Full article

Riverine and pluvial flooding triggered by extreme monsoon rainfall is intensifying under climate change, yet flood-risk products in many coastal municipalities remain too coarse for parcel-scale prevention and climate-adaptive planning. This study presents a 1 m LiDAR–GIS flood susceptibility framework validated against consecutive record-breaking floods in Dangjin City, South Korea (July 2024: 214.6 mm; July 2025: 377.4 mm). Five terrain parameters—elevation, slope, topographic wetness index, flow accumulation, and distance to stream—were integrated into a weighted Flood Susceptibility Index ($\mathrm{FSI}=0.20\cdot \widehat{E}+0.30\cdot \widehat{S}+0.25\cdot \widehat{T}+0.15\cdot \widehat{F}+0.10\cdot \widehat{D}$) and classified into four risk strata using K-means clustering (k = 4), identifying a high-risk zone of 0.3119 km² (5.00% of the 6.18 km² analysis domain). A Monte Carlo sensitivity analysis (n = 5000; ±0.10 weight perturbation) confirmed classification robustness (CV = 5.21%, mean Pearson r = 0.992). Static bathtub inundation scenarios (Δh = 0.5–2.0 m above the 5th-percentile baseline elevation of 13.29 m AMSL) produced footprint expansion from 0.370 to 0.572 km², capturing all nine observed flood inventory points at the 2.0 m threshold, with flow-connectivity analysis confirming that 91.7–93.1% of predicted inundation is hydraulically connected to the D8-derived stream network. Spatial validation yielded a combined IoU of 6.51%, with a progressive increase from 3.33% (2024) to 6.50% (2025) confirming that the FSI correctly tracks flood-extent expansion with increasing rainfall intensity. Relying exclusively on topographic data and standard GIS algorithms, the framework supports scientifically grounded flood risk governance in data-limited municipalities, directly aligned with SDG 11, SDG 13, and Sendai Framework Target B. Full article

Screw compressors are critical equipment in oil and gas production and transportation, where efficiency losses caused by rotor geometry, inlet pressure pulsations, and harsh climatic conditions can accumulate into substantial annual energy penalties and reliability degradation. This study provides a quantitative assessment of these coupled effects within a unified multiphysics framework that combines time-accurate transient CFD simulations based on a fixed Cartesian immersed-boundary formulation with a climate-calibrated offline physics-based digital twin—functioning as a digital shadow with one-way data flow from archival SCADA records—a reduced-order seasonal model with no real-time updating, calibrated against a full calendar year of SCADA records and validated against a held-out cold-season dataset (October–December 2022, T_amb = −15 to +8 °C); summer-period predictions rely on calibrated extrapolation beyond the validation window—an integration not previously demonstrated for oil-flooded screw compressors. Two rotor profile configurations (Type A and Type B) were analyzed to quantify geometry-driven differences in static pressure distribution, leakage tendency, and pulsation sensitivity. Transient suction conditions were modeled using harmonic and quasi-random inlet pressure disturbances to evaluate pressure amplification, phase lag, leakage intensification, and efficiency degradation. Seasonal performance was assessed by integrating temperature-dependent gas properties, oil viscosity behavior, and external heat transfer into an annual climatic load framework. The results show that inlet oscillations are amplified inside the chambers (pressure amplification factor П_p ≈ 1.95; П_p up to 2.3 under quasi-random excitation), reducing mass flow and volumetric efficiency by 8–10% and decreasing polytropic efficiency from 0.78 to 0.69–0.71, while increasing leakage by up to 27% and raising peak contact pressures to 167–171 MPa. Seasonal variability (+30 to −30 °C) increased suction density by 38% but raised drive power by ~9% due to viscosity-driven mechanical losses, producing an energy penalty up to 10.8% and an estimated annual additional consumption of approximately 186 MWh per compressor, decomposed as: cold-season contribution ~113 MWh (±10 MWh, directly field-validated against October–December 2022 SCADA data) and summer-season contribution ~51 MWh (calibrated extrapolation; additional uncertainty unquantified and not included in the ±10 MWh bound). The full annual figure of 186 MWh should be interpreted as a model-based estimate rather than a fully validated result. These findings demonstrate that rotor design optimization and mitigation of nonstationary suction effects, coupled with climate-aware offline physics-based digital shadow operation, represent high-priority levers for improving efficiency and reducing energy penalties in field conditions; reliability implications require further validation against summer-season field measurements. Full article

Human activities and climate change are influencing the survival and distribution of species, threatening the current distribution pattern of biodiversity and potentially leading to the “sixth mass extinction.” The forest musk deer (Moschus berezovskii) is among the most numerous and widely distributed musk deer species in China. However, its habitat is severely threatened by human activities and climate change. Due to the lack of field surveys and research data, it is difficult to assess the threats posed by human activities and climate change effectively. In this study, we integrate the new records of forest musk deer with climate and human activity data, and apply the MaxEnt species distribution model to evaluate the impact of human activities and climate change on the forest musk deer under current conditions and future scenarios (SSP1-2.6 and SSP5-8.5 for the 2030s, 2050s, and 2070s). Our results showed that the forest musk deer prefer areas with high vegetation cover (NDVI > 0.7), low GDP, and low levels of human activity disturbance. The areas of high-suitability habitats are 90.10 × 10⁴ km², 72.85 × 10⁴ km², and 30.43 × 10⁴ km², respectively. The optimal climatic conditions are an annual precipitation (BIO12) of 750–1500 mm and a seasonal temperature variation (BIO4) of 500–600. Their occurrence probability is highest at elevations between 1500 and 3000 m. Under the current climate conditions, the area of high-suitability habitats is estimated at 5.54 × 10⁴ km², primarily distributed across central–northern Sichuan, northwestern Guangxi, and southern Gansu. Under the future climate scenarios, low and medium-suitability habitats are projected to shrink to varying degrees, whereas the high-suitability area is expected to expand, particularly under the SSP5-8.5-2030s scenario where it is projected to increase by 2.88 × 10⁴ km². The centroid of suitable habitat is projected to shift toward higher-elevation areas in northwestern China, with regional hotspots emerging in southwestern regions such as central–northern Sichuan and northwestern Guangxi. These elevational and distributional shifts highlight the vulnerability of current habitats and the importance of adaptive conservation strategies to strengthen species protection, including continuously advancing forest protection programs, mitigating the impact of human activities in high-altitude areas, and strengthening the protection of key areas in the southwestern region. Full article

Climatic niche breadth is defined as the range of climatic conditions (e.g., temperature and precipitation) under which a species occurs. However, the relationship between niche breadth variation and climatic factors remains poorly studied, and existing results require more general testing. We studied spatial patterns of variation in climatic niche breadths in lizards of the family Agamidae and compared patterns within and across regions to see if they parallel or differ from each other using geo-referenced occurrence records, climatic data and phylogenetic comparative methods. We found that (1) species in warmer environments have narrower temperature niche breadths; (2) precipitation niche breadths are positively correlated with precipitation niche position, and also with temperature niche breadths; and (3) most of the variation in temperature niche breadths is explained by within-locality variation in climatic conditions, whereas most of the variation in precipitation niche breadths is explained by among-locality variation. The patterns of climatic niche breadth in agamids are consistent across regional and global scales, similar to those in other amphibians and reptiles. This suggests that this is a widespread phenomenon among ectothermic vertebrates. Full article

This research investigates the relationships between the parameters of the GR4J hydrological model and a set of morphometric descriptors, climatic indices, land-cover characteristics, and soil properties across the Caquetá River Basin (Colombia). Twelve limnimetric–limnographic gauges with consistent records for the period 2001–2022 were selected for model calibration and validation. The corresponding sub-watersheds were delineated and characterized in terms of geomorphometry, vegetation cover, and soil permeability. According to that, the morphometric assessment focused on estimating key geomorphometric parameters, while land-cover descriptions utilized NDVI data. Soil type identification was based on the average approximate permeability across each analyzed sub-watershed. Model calibration was performed using the Differential Evolution Markov Chain (DE-MC) algorithm with 8000 simulations, forced by CHIRPS satellite precipitation and ERA5 potential evaporation data. Relationships between GR4J parameters and watershed attributes were assessed using Spearman’s rank correlation and curve-fitting analyses. The results reveal strong and consistent relationships between GR4J parameters (X1–X4) and key morphometric variables, including basin perimeter, circularity ratio, main channel length, and channel slope. Coefficients of determination ranged from 0.80 to 0.98, highlighting the potential for parameter regionalization based on physiographic and environmental descriptors. Full article

Floods are among the extreme events associated with climate variability in the Lacramarca River basin, located in the department of Ancash, Peru. Meteorological phenomena such as El Niño during the periods 1982–1983 and 1997–1998, as well as the Coastal El Niño in 2017, constitute key reference events that motivated the development of the present study, based on a case study conducted in the area between the rural settlements of Santa Clemencia and Pampadura. This research is based on maximum precipitation data derived from historical climate records and from the climate scenarios ACCESS 1-3, HadGEM2-ES, and MPI-ESM-MR, as well as the median projected scenario for 2050, obtained from the National Meteorology and Hydrology Service of Peru (SENAMHI) data platform. This information was analyzed considering the spatial location of the basin and its position relative to the area of interest, using Intensity–Duration–Frequency (IDF) curves. To demonstrate the changes in the river hydrological behavior before and after the 2017 Coastal El Niño event, a Random Forest modeling approach was applied using Sentinel-2 satellite imagery. Design peak discharges for return periods of 50, 100, and 140 years were estimated using the HEC-HMS software. Hydraulic simulation of the Lacramarca River basin, carried out using HEC-RAS version 6.7 beta 3 and IBER version 3.3.1 software, made it possible to identify flood-prone areas affecting agricultural land and areas adjacent to population centers, covering 149,000 m² and 172,000 m² for return periods of 100 and 140 years, respectively, based on information from the historical scenario. In contrast, using data from the 2050 projection scenario, affected areas of 242,000 m² and 323,000 m² were estimated for the same return periods. Full article