Influenced by the complex geological conditions in mountainous region, micro-pile foundation for transmission line faces the risk of insufficient bearing performance. It is important to study the risk suppression measures of micro-pile foundation and its assessment method to promote the construction of transmission lines in mountainous regions. Firstly, the mechanical simulation model of pile-soil system for the micro-pile foundation is established in this paper, and the field test is carried out to verify the accuracy of the simulation model, thus the ultimate load of the micro-pile foundation is determined according to the current code requirement for maximum displacement in case of damage to the group pile foundation. Secondly, to address the subjectivity of traditional methods, an improved Likelihood-Exposure-Consequence (LEC) method is proposed. Its novelty lies in constructing a quantitative displacement-risk mathematical mapping, directly linking the physical limit state (maximum displacement) to the risk likelihood factor. Thirdly, structural risk reduction measures for the micro-pile foundation using micro-expanded pile foundation and micro-inclined pile foundation are proposed, and the ultimate load of the traditional micro straight pile foundation is used as an excitation to carry out the simulation of the bearing performance of the two improved micro-pile foundations, and the maximum displacements of the two improved micro-pile foundations are calculated. Finally, based on the proposed improved LEC method, the risk values-defined in the LEC framework as the quantitative product of Likelihood (L), Exposure (E), and Consequence (C)-and risk classes of the two improved micro-pile foundations are calculated and compared with the conventional micro straight pile. The results demonstrate that the proposed strategies significantly reduce the safety risk class, providing a robust, quantifiable basis for optimizing foundation designs in complex mountainous terrain.

Mountainous regions, particularly the Lesser Himalayas, face persistent slope stability challenges due to complex geological formations and extreme climatic variability. This study investigates the potential of Nano-silica (NS) as an eco-efficient soil stabilizer for enhancing the mechanical behaviour of fine-grained soils in these terrains. Consolidated undrained (CU) triaxial tests were performed on clay of intermediate plasticity (CI), silt of intermediate plasticity (MI), and low-plasticity clay-silt (CL-ML) soil types treated with different NS contents and subjected to various curing durations. To develop reliable predictive insights, both linear and non-linear regression models were constructed using essential geotechnical parameters, including cohesion, internal friction angle, and the pore-water pressure ratio. A novel model simplification process was employed to derive explicit closed-form equations for the Factor of Safety (FoS), retaining only the most influential terms. The non-linear models demonstrated high predictive accuracy, with R2 values exceeding 0.97, outperforming their linear models for all the soil types stabilized with NS. These interpretable regression models offer a practical tool for slope stability assessments in NS-stabilized soils. The integration of material innovation with simplified computational models contributes to resilient and sustainable infrastructure design in data-scarce highland regions.

Huizhou covered bridges represent a unique and irreplaceable component of China′s architectural heritage, yet they are increasingly threatened by flash floods. In the Huizhou region, complex mountainous terrain, concentrated intense rainfall, and structural aging jointly exacerbate flood damage risks. Existing flood risk assessment approaches often prioritize external hydrodynamic hazards or assume linear additive effects, overlooking the complex interactions among inherent structural and physical attributes. To address this limitation, this study integrates Random Forest (RF) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to develop a flood risk assessment framework capable of capturing both nonlinear relationships and configurational (asymmetric) causal mechanisms. Based on field investigations of 89 covered bridges and 116 documented damage cases from 2020 to 2024, the RF model identifies six key risk factors (ACC = 0.79, AUC = 0.87), several of which exhibit pronounced nonlinear and threshold effects. Building on these results, fsQCA further reveals eight equivalent configurational pathways leading to covered bridge damage (solution coverage = 0.66, solution consistency = 0.94), highlighting multiple causal combinations rather than a single dominant driver. The results demonstrate that the disaster resilience of covered bridges emerges from interactions among structural characteristics, management conditions, and spatial scale attributes, rather than from any individual factor alone. Accordingly, this study advocates a shift in protection strategies from conventional “one-size-fits-all” structural reinforcement toward risk-pattern-oriented, precision-based non-structural interventions. By combining predictive modeling with configurational causal analysis, this research provides a system-level understanding of flood-induced damage mechanisms and offers actionable insights for flood risk mitigation and sustainable conservation of covered bridge heritage in Huizhou and comparable regions worldwide.

Tracing the causes of fluoride enrichment in the groundwater sources of the Upper Bhavani River Basin, Southwest India–An integrated approach for its management in mountainous terrains

This study examines the spatial differentiation patterns and formation mechanisms of intangible cultural heritage in Chongqing within a cultural geography framework by integrating Geographic Information System spatial analysis and the geographic detector model. Focusing on a mountainous urban context, the research quantitatively assesses the combined effects of natural environmental constraints and socioeconomic drivers on the spatial distribution of ICH. The results indicate that Chongqing’s ICH displays a clear core–periphery structure shaped by the interaction between cultural ecological adaptation and regional development dynamics. Spatially, a “one core, two wings” pattern is identified, in which the main urban area functions as a highly concentrated core, while northeastern and southeastern Chongqing form two distinct cultural zones with differentiated heritage characteristics. Significant heterogeneity is observed in the clustering patterns of different ICH categories, suggesting varying sensitivities to environmental and socioeconomic conditions. Factor detection results show that total retail sales of consumer goods constitute the most influential socioeconomic determinant of ICH distribution, whereas average digital elevation model elevation represents a key natural constraint in mountainous terrain. Interaction analysis further reveals pronounced nonlinear enhancement effects, particularly between economic development level and government support, indicating that institutional and economic forces jointly intensify spatial differentiation processes. By elucidating the dominant drivers and interaction mechanisms underlying ICH spatial patterns, this study advances the understanding of cultural space evolution in mountainous cities. Methodologically, it demonstrates the applicability of combining GIS-based spatial analysis with geographic detector techniques in cultural heritage research, while practically providing quantitative evidence to support culturally sensitive spatial planning and targeted conservation strategies in complex urban-mountain environments.

The Yongin Seori site not only revealed that the site began as a kiln producing celadon in the early Goryeo Dynasty, but also transitioned to producing early white porcelain. Furthermore, various physical evidence has provided insight into the nature of early white porcelain. Furthermore, the Yongin Seori site is the first brick kiln discovered in Korea, producing celadon and white porcelain from the mid-9th century to the 12th century. The site is considered a crucial site for elucidating the origins and development of Goryeo porcelain, as it also uncovered a well-defined archaeological deposit of waste. The Yongin Seori Jungdeok kiln site shares similar characteristics with the Wonsan-ri kiln site, as numerous vessels were excavated from the second layer of sedimentary deposits, corresponding to the pre-construction kiln period, and the third layer, representing the full-scale operation of the earthen kiln. However, the absence of the craftsman's inscriptions found at the Wonsan-ri kiln site highlights the early completion of celadon vessels, sufficiently large for use in royal tombs. This difference ultimately indicates that the Wonsan-ri site achieved the level of perfection required for early celadon vessels, even for royal tombs. Therefore, the Yongin Seori kiln site can be considered a test kiln, marking the beginning of ritual craftsmanship. In other words, a large-scale state-run porcelain kiln was established in the Seori area to produce ritual vessels needed by the royal family in the early Goryeo Dynasty. An examination of the fortress walls of the Yongin area and the maps of the Goryeo Dynasty reveals that transportation routes branched out around the Guseong area. All routes from Gaeseong and Namgyeong to the southern regions passed through Guseong. This demonstrates its strategic importance as the southern gateway to Gaeseong. The Yongin area, with its mountainous terrain and limited flatland, does not offer significant productivity. However, its strategic location afforded it the potential to become a hub for the exchange of people and goods. The administrative districts governing Yonggu County and Cheoin Bugok differ, with one being Gwangju Mok and the other being Suju (Suwon). While both regions are considered peripheral areas, far from the central administrative center, their shared geographic, cultural, economic, and transportation environments likely contributed to their merger into Yongin in the early Joseon Dynasty. In particular, from a ceramics perspective, the geographical proximity of the two regions likely facilitated the smooth maintenance of key elements of the industry, such as raw material and fuel supply, distribution, and transportation. However, it is believed that the operation of the Seori kiln in the early Goryeo period was primarily controlled by a higher-level state than the Suju, and that the production system shifted to a small-scale industrial system after the mid-Goryeo period. Ultimately, rather than being divided by administrative district, it would be more appropriate to recognize the Yongguhyeon and Cheoinbugok regions as integrated cultural and economic zones, a transportation hub in southern Gyeonggi Province, connecting Gaeseong and Namgyeong to the south of Chungcheong Province. This is especially true if the Yongin Seori Jungdeok kiln was a test kiln, as it would have been possible to transport large quantities of ceremonial vessels by land, rather than by water or sea to Gaeseong. There is a prevailing tendency to link the operation of the Seori kiln to local powers. However, if the royal family or other state had been directly involved in its operation, their intervention would have been impossible. The local seigneurs who controlled the surrounding area around Seori were merely consumers of the porcelain produced at the Seori kiln.

The Lake Sevan basin is particularly sensitive to climate change due to its continental climate and mountainous terrain, which collectively amplify climatic impacts. This study aimed to assess the influence of climate change on the thermal dynamics of the basin by analyzing both historical and projected temperature variations. Over the past three decades, the region has experienced a marked rise in air temperatures. Seasonal variability revealed distinct contrasts between winter and summer, with winter exhibiting greater fluctuations, ranging from 1.67 to 2.41 °C, compared to the more stable summer range of 0.81 to 1.41 °C. An analysis of heat inflow and outflow patterns demonstrated a moderating effect of Lake Sevan on temperature extremes. Stations, located near the lake, recorded lower levels of heat inflow and outflow, indicating that the lake’s thermal inertia helps buffer seasonal temperature extremes. In contrast, stations situated farther from the lake exhibited more pronounced fluctuations, reflecting the absence of this stabilizing influence. These results underscore the lake’s critical role in modulating the local climate by dampening extreme thermal variations. Additionally, comparative analysis of air and water temperature trends revealed that, while both exhibit warming, air temperatures show greater interannual variability. In contrast, water temperatures remained more stable, particularly during winter, due to the lake’s thermal inertia. Future climate projections for the Lake Sevan region, based on CMIP6 (Coupled Model Intercomparison Project phase 6) ensemble outputs under four Shared Socioeconomic Pathways (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), suggest a persistent warming trend throughout the 21st century. We project that the most significant increases are expected during summer months, with an anticipated mean annual temperature rise of up to 6 °C by the end of the century under the high-emission scenario (SSP5–8.5).

Introduction Rural areas are typically characterized by poor traffic conditions, less road supervision, and more illegal driving behaviors. These factors lead to significantly higher mortality and increased rates of road traffic crashes in rural areas than in urban areas. Therefore, an in-depth study of traffic risk factors in the rural areas of developing countries with large rural populations and extensive rural road coverage is particularly urgent and important. Method Based on the report data of 38,458 traffic crash occurring in rural areas of Guangdong Province between 2006 and 2014 in the Road Traffic Crash Database of the Ministry of Public Security of China, the logit model was used to evaluate the impact of four factors—drivers, vehicles, roads, and environment—on at-fault crash behavior and serious casualties in rural areas. Furthermore, the risk factors for rural and urban areas were comparatively analyzed. Results In rural areas, male drivers, drivers without valid licenses, drivers of unsafe vehicles and drivers of trucks are more likely to cause at-fault crashes and cause serious casualties, as are drivers impacted by certain conditions, including sand and gravel roads, mountainous terrain, and nighttime without street lighting. In urban areas, crashes involving sand and gravel roads and single-vehicle crashes have lower casualties, but migrant workers and self-employed individuals are more likely to die or be seriously injured in urban road crashes. Conclusion To reduce the impact of road traffic risk factors on at-fault crash behaviors and serious casualties in the rural areas of developing countries, targeted measures should be implemented for drivers, vehicles, and roads. These measures may include focusing on illegal drivers, trucks, and unsafe vehicles for enhanced supervision, as well as prioritizing the construction and improvement in transportation infrastructure such as roads, lighting, and safety mechanisms in rural areas.

In recent years, existing models in complex geographical scenarios (such as densely built urban areas and fragmented mountainous terrains), suffer from insufficient feature fusion accuracy and poor scene adaptability, leading to blurred edges and loss of details in super-resolved images. This limitation hinders their practical applications in fields like emergency disaster relief (e.g., rapid assessment of building damage in disaster areas) and agricultural yield estimation (e.g., precise identification of crop growth conditions). Many convolutional neural network (CNN)-based methods have made significant progress in single-image super- resolution tasks. However, CNNs only excel in local feature extraction while lacking in global feature modeling capabilities. To address this issue, we integrate CNNs(CNN), leveraging their local feature extraction strengths, with Transformers, which excel in global dependency modeling, to provide a novel approach for remote sensing image super-resolution. The CNN-Transformer aggregation mechanism enhances the model's adaptability to multi-scene remote sensing images, promoting the deep application of remote sensing super-resolution technology in practical operations (e.g., rapid emergency monitoring and precision agricultural yield estimation), thereby maximizing the value of remote sensing data.

Abstract At midnight of July 25, 2024, Typhoon Gaemi made landfall in Taiwan and became the first tropical cyclone (TC) at the severe typhoon intensity level to hit the island directly since Typhoon Nepartak in 2016. What is so special about Gaemi is its unusual looping motion occurring near the coast of Taiwan just a few hours before its final landfall. Such a deflection of track significantly prolonged the length of time in which Gaemi impacted Taiwan, leading to unexpected and devastating damages. Particularly, the central and southern parts of Taiwan suffered badly from sustained heavy rainfalls which subsequently led to serious floods and landslides. Here, we demonstrate that this rare looping phenomenon can be explained by the channeling effect as the TC approaches the mountainous terrains of Taiwan, which creates a low-to-mid-level northerly jet at the western side of the TC that induces it to move southwards. In the later stage of the looping track, a southwesterly corner flow develops, contributing to Gaemi’s subsequent northward turning. The potential vorticity budget further supports that both the southward deflection and northward turning are both dominated by the horizontal advection term associated with these terrain-induced asymmetric flows, providing a clear dynamical explanation for the entire looping process.

Gravity waves (GWs) are an important dynamic process in the planetary atmosphere. They are typically excited by convection, topography, or other sources from the lower atmosphere and propagate upwards. The GWs have a significant effect on the global atmospheric circulation on Mars. However, the lack of high-resolution data from previous observations has resulted in an insufficient understanding of GWs in the Martian atmosphere, particularly in terms of its global distribution and long-term evolution characteristics at different altitudes. Based on multiple years of Mars Climate Sounder (MCS) limb observations on board the Mars Reconnaissance Orbiter (MRO), we conducted a detailed study of the global distribution, seasonal and interannual variations in Martian atmospheric GWs with vertical wavelengths ranging from 9 to 15 km at three different altitude ranges, i.e., the low-altitude range of 200–20 Pa (Lp, ~10–30 km), the mid-altitude range of 20–2 Pa (Mp, ~30–50 km), and the high-altitude range of 2–0.2 Pa (Hp, ~50–70 km). The results indicate complex regional and north–south differences, as well as night–day variations, in the spatial distribution of GWs. Particularly, a three-wave structure of the GW activity is observed over mountainous regions in the mid-to-low latitudes of the Northern Hemisphere. The peak longitude range of this structure closely matches the mountainous terrain. In addition, our results reveal the presence of bands of GW aggregations in the mid- to-high latitudes of the Northern Hemisphere in the Mp and Hp layers, which may be caused by the instability of the polar jet. There are also obvious seasonal and interannual variations in GW activities, which are related to topography, polar jets, and large dust storms. The interannual variations in GWs imply that, in addition to the well-known large seasonal dust storms, complex interannual variations in atmospheric activity over the polar jets and in the complex topography at mid-to-low latitudes on Mars may also exist, which deserve further studies in the future.

Fucheng-1 is China’s first commercial synthetic aperture radar (SAR) satellite equipped with interferometric capabilities. Since its launch in 2023, it has demonstrated strong potential across a range of application domains. However, a comprehensive and systematic evaluation of its overall performance, including its time-series monitoring capability, is still lacking. This study applies the Small Baseline Subset (SBAS-InSAR) method to conduct the first systematic processing and evaluation of 22 Fucheng-1 images acquired between 2023 and 2024. A total of 45 potential landslides were identified and subsequently validated through field investigations and UAV-based LiDAR data. Comparative analysis with Sentinel-1 and ALOS-2 indicates that Fucheng-1 demonstrates superior performance in small-scale deformation identification, temporal-variation characterization, and maintaining a high density of coherent pixels. Specifically, in the time-series InSAR-based potential landslide identification, Fucheng-1 identified 13 small-scale potential landslides, whereas Sentinel-1 identified none; the number of identifications is approximately 2.17 times that of ALOS-2. For time-series subsidence monitoring, the deformation magnitudes retrieved from Fucheng-1 are generally larger than those from Sentinel-1, mainly attributable to finer spatial sampling enabled by its higher spatial resolution and a higher maximum detectable deformation gradient. Moreover, as landslide size decreases, the advantages of Fucheng-1 in deformation identification and subsidence estimation become increasingly evident. Interferometric results further show that the number of high-coherence pixels for Fucheng-1 is 7–8 times that of co-temporal Sentinel-1 and 1.1–1.4 times that of ALOS-2, providing more high-quality observations for time-series inversion and thereby supporting a more detailed and spatially continuous reconstruction of deformation fields. Meanwhile, the orbital stability of Fucheng-1 is comparable to that of Sentinel-1, and its maximum detectable deformation gradient in mountainous terrain reaches twice that of Sentinel-1. Overall, this study provides the first systematic validation of the time-series InSAR capability of Fucheng-1 under complex terrain conditions, offering essential support and a solid foundation for the operational deployment of InSAR technologies based on China’s domestic SAR satellite constellation.

To address the insufficient exploration ability, susceptibility to local optima, and limited convergence accuracy of the standard Student Psychology-Based Optimization (SPBO) algorithm in three-dimensional UAV trajectory planning, we propose an enhanced variant, Enhanced SPBO (ESPBO). ESPBO augments SPBO with three complementary strategies: (i) Time-Adaptive Scheduling, which uses normalized time () to schedule global step-size shrinking, Gaussian fine-tuning, and Lévy flight intensity, enabling strong early exploration and fine late-stage exploitation; (ii) Mentor Pool Guidance, which selects a top-K mentor set and applies time-varying guidance weights to reduce misleading attraction and improve directional stability; and (iii) Directional Jump Exploration, which couples a differential vector with Lévy flights to strengthen basin-crossing while keeping the differential step bounded for robustness. Numerical experiments on CEC2017, CEC2020 and CEC2022 benchmark functions compare ESPBO with Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Improved multi-strategy adaptive Grey Wolf Optimization (IAGWO), Dung Beetle Optimization (DBO), Snake Optimization (SO), Rime Optimization (RIME), and the original SPBO. We evaluate best path length, mean trajectory length, standard deviation, and convergence curves and assess statistical stability via Wilcoxon rank-sum tests (p = 0.05) and the Friedman test. ESPBO significantly outperforms the comparison algorithms in path-planning accuracy and convergence stability, ranking first on both test suites. Applied to 3D UAV trajectory planning in mountainous terrain with no-fly zones, ESPBO achieves an optimal path length of 199.8874 m, an average path length of 205.8179 m, and a standard deviation of 5.3440, surpassing all baselines; notably, ESPBO’s average path length is even lower than the optimal path length of other algorithms. These results demonstrate that ESPBO provides an efficient and robust solution for UAV trajectory optimization in intricate environments and extends the application of swarm intelligence algorithms in autonomous navigation.

HighlightsWhat are the main findings?This study applies the SETP-EMI method for the first time to plateau mountainous regions with dense vegetation, demonstrating its ability to overcome severe coherence loss.The integrated DS-InSAR framework significantly improves distributed scatterer density, phase stability, and deformation continuity compared with PS-InSAR and SBAS-InSAR.What are the implications of the main findings?The demonstrated performance of SETP-EMI in challenging high-altitude, vegetation-covered terrain indicates its strong potential for large-scale geohazard monitoring in complex mountainous environments.The method provides an effective technical route for enhancing early-warning capability of landslides where conventional InSAR approaches typically fail due to low coherence.Landslide deformation monitoring plays a critical role in geohazard prevention and risk mitigation in mountainous regions, where timely and reliable deformation information is essential for early warning and disaster management. Monitoring landslide deformation in mountainous areas remains a persistent challenge, largely due to rugged topography, dense vegetation cover, and low interferometric coherence—factors that substantially limit the effectiveness of conventional InSAR methods. To address these issues, this study aims to develop a robust time-series InSAR framework for enhancing deformation detection and measurement density under low-coherence conditions in complex mountainous terrain, and accordingly introduces the Sequential Estimation and Total Power-Enhanced Expectation–Maximization Inversion (SETP-EMI) approach, which integrates dual-polarization Sentinel-1 SAR time series within a recursive estimation framework, augmented by polarimetric coherence optimization. This methodology allows for dynamic assimilation of SAR data, improves phase quality under low-coherence conditions, and enhances the extraction of distributed scatterers (DS). When applied to Zhenxiong County, Yunnan Province—a region prone to geohazards with complex terrain—the SETP-EMI method achieved a landslide detection rate of 94.1%. It also generated approximately 2.49 million measurement points, surpassing PS-InSAR and SBAS-InSAR results by factors of 22.5 and 3.2, respectively. Validation against ground-based leveling data confirmed the method’s high accuracy and robustness, yielding a standard deviation of 5.21 mm/year. This study demonstrates that the SETP-EMI method, integrated within a DS-InSAR framework, effectively overcomes coherence loss in densely vegetated plateau regions, improving landslide monitoring and early-warning capabilities in complex mountainous terrain.

Most population-aggregated cities in developing countries are facing severe air pollution due to high concentrations of PM2.5 and O3, which could be associated with the impacts of local mesoscale circulations. The river land breeze (RLB) and mountain valley breeze (MVB) circulations made PM2.5 and O3 pollution causes more complicated in inland regions. By combining monitoring and simulating data from the inland region, this study analyzed the influence of local mesoscale circulations on pollutant concentrations and their underlying mechanisms. The daily average and peak concentrations of pollutants in RLB days and MVB days were different from those in non-RLB days and non-MVB days, indicating that local mesoscale circulations exerted distinct influences on regional pollutant concentrations. Moreover, hourly PM2.5 concentrations had a notable reduction during winter in RLB days when compared to non-RLB days (1.6-18.7 μg/m3), meanwhile, they increased the most during spring (1.3-7.0 μg/m3) and decreased the most during winter (2.5-14.3 μg/m3) in MVB days when compared to non-MVB days. Hourly O3 concentrations increased the least during winter in RLB days compared to non-RLB days (6.6-22.4 μg/m3), meanwhile, they showed the largest increase during spring (32.3 μg/m3) and the smallest increase during winter in MVB days compared to non-MVB days (13.2 μg/m3). Both the RLB and MVB could disperse particulate horizontally through the lower branches, with the extent of dispersion varying according to wind speeds in different seasons. The recirculation dominated by stronger branches (river/valley breezes) could enhance the formation of both O3 and secondary PM2.5 by prolonging the residence time within the region and promoting the pollutant mixing. Based on those diverse contributions, targeted management strategies will be highly desirable in regions over riverine and mountainous terrains in the inland region in Yichang, China, and similar cities around the world.

Shallow landslides are pervasive geomorphic threat in mountain terrain, especially where intense precipitation, steep relief, and land-use changes overlap. This research investigates the geotechnical and hydrological behavior of hillslope materials under three types of land uses cultivated (terraced), dispersedly vegetated, and dense forest areas in the landslide-prone area of Rishikhola in the Darjeeling-Sikkim Himalaya. Soil samples were tested for grain size distribution, bulk density, porosity, moisture content, pH and compaction properties. Results indicate that cultivated areas show greater compaction (bulk density up to 1.62 g/cm³), reduced porosity (~ 39.6%), higher moisture content (> 32%), and more acidic pH (5.3–5.5) and these conditions collectively diminish soil strength and increase pore-water pressure, thereby predisposing the slopes to heightened instability under intense or prolonged rainfall.. Dense forest areas, on the other hand, exhibited comparatively lower compaction, balanced moisture regimes, and near-neutral pH values (5.95–6.28). These conditions contribute to enhanced slope stability through improved infiltration, microbial processes, and root reinforcement in the dense forrest areas. The results highlight a distinct gradient of landslide susceptibility according to land use patterns. This research also presents transferable knowledge for other active tectonic and monsoonal-influenced mountain belts worldwide, where the interaction between land use, soil properties and slope hydrology plays a critical role in shallow landslide susceptibility. The findings reaffirm the need to incorporate ecological restoration into mitigation strategies in the Himalayan and similar high-relief environments globally.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-30543-y.

Rack vehicles, specialized for large-slope routes, are increasingly vital to transportation systems in mountainous and complex terrains. A dynamic model of a rack vehicle–track (rack) system with variable slopes was established based on vehicle–track coupled dynamics theory. This model accounts for vehicle load, track slope, wheel–rail contact, gear-rack meshing, and track irregularity. The dynamic characteristics and comfort of rack vehicles under empty and fully loaded conditions across varying slopes were evaluated. Key metrics include acceleration, contact forces, meshing forces, derailment coefficient, and stability index. Results show that steeper slopes lead to increased longitudinal acceleration, meshing force, derailment coefficient (max: 0.43), and stability index (max: 2.96) for an empty vehicle, with reduced vertical acceleration and contact forces. For a fully loaded vehicle, higher values were observed: wheelset acceleration (3.3 m/s2), contact forces (119.9 kN vertical, 30.7 kN lateral), meshing forces (348 kN longitudinal, 220 kN lateral), and derailment coefficient (0.57), indicating elevated derailment risk. However, its stability index (2.72) is lower than that of the empty vehicle, reflecting enhanced smoothness due to greater mass and rigidity. These results provide a theoretical foundation for optimizing rack vehicle design and operation on complex slopes, improving safety and comfort.

Vibration characteristics of a terrain-adaptive agricultural chassis for hilly and mountainous terrain

Given the complex mountain terrain and the insufficient network of meteorological stations, assessing and forecasting avalanche hazard in the Magadan Region is a pressing issue. This study examines patterns in snow cover characteristics (average and maximum snow depth, dates of establishment and disappearance) at 1) gauges located on mountain passes or at altitude " 900 m along the Kolyma and Tenkinskaya highways, and 2) at the nearest meteorological stations of the Roshydromet in 2022-2025. For two test sites, avalanche runout distanceswere calculated according to regulatory documents based on data from the monitoring network and Roshydromet stations in 2025. This study utilizes the data from the automatic snow cover monitoring network, which was installed in the Magadan Region in 2022-2025 (altitude range of 162–1232 m). Data from field studies were also used. Avalanche characteristics were calculated in accordance to regulatory documents. For the first time, an automatic snow cover monitoring network was installed in the Magadan Region, and data obtained in 2022–2025 were analyzed. This revealed significant discrepancies in snow characteristics between Roshydromet stations and high-altitude sections of the monitoring network. It was found that snow depth on mountain passes can be 2–3 times higher than that of the reference station, and the duration of snow cover can increase by 25 days or more. The insufficiency of the existing Roshydromet network for accurate avalanche hazard forecasting was demonstrated. Using avalanche start zones near the Gusakova Pass as an example, it was demonstrated that applying data from the automated network allows for accurate assessment of avalanche runout distance, whereas calculations based on meteorological station data underestimate actual figures by 1.5–2 times. The obtained results can form the basis for recommendations for developing a monitoring system and avalanche protection measures along the most dangerous sections of Magadan Region's highways.

Abstract Extreme precipitation events can rapidly reshape mountain landscapes, even in tectonically inactive regions like the Southern Appalachians. Here, we illustrate how discrete storm events drive rapid geomorphic change with a repeat‐lidar analysis of impacts from Hurricane Helene (September 2024) in the Hickory Nut Gorge, North Carolina, USA. Airborne lidar collected before and after Helene reveals characteristic patterns of landslide initiation, sediment delivery, and river channel evolution. Our observations augment the current understanding of landslide reactivation, highlight debris flows as a key process of coarse sediment transport, and demonstrate how episodic flooding can reshape river channel geometry and roughness. Geomorphic signatures of extreme events quickly obscure as vegetation regrows and infrastructure is repaired, underscoring the need for both pre‐event and rapid postevent lidar to detect and quantify change. Together, these insights clarify how infrequent, high‐intensity storms drive both immediate landscape change and long‐term geomorphic evolution in steep mountain terrain.