Search Results (304)

This study assesses the impact of climate change and glacier retreat on river runoff in the high-altitude Terek River Basin using the physically based ECOMAG hydrological model. Sensitivity experiments examined the influence of glaciation, precipitation, and air temperature on runoff variability. Results indicate that glacier retreat primarily affects streamflow in upper reaches during peak melt (July–October), while precipitation changes influence both annual runoff and peak flows (May–October). Rising temperatures shift snowmelt to earlier periods, increasing runoff in spring and autumn but reducing it in summer. The increase in autumn runoff is also due to the shift between solid and liquid precipitation, as warmer temperatures cause more precipitation to fall as rain, rather than snow. Scenario-based modeling incorporated projected glacier area changes (GloGEMflow-DD) and regional climate data (CORDEX) under RCP2.6 and RCP8.5 scenarios. Simulated runoff changes by the end of the 21st century (2070–2099) compared to the historical period (1977–2005) ranged from −2% to +5% under RCP2.6 and from −8% to +14% under RCP8.5. Analysis of runoff components (snowmelt, rainfall, and glacier melt) revealed that changes in river flow are largely determined by the elevation of snow and glacier accumulation zones and the rate of their degradation. The projected trends are consistent with current observations and emphasize the need for adaptive water resource management and risk mitigation strategies in glacier-fed catchments under climate change. Full article

Both studies and conservation of mountain waters are essential because of the primary role of mountains as “natural water towers” for the preservation and optimized exploitation of water reserves. In particular, under climate change stresses which induce reductions in rain and snow precipitation, especially in areas with rain-snow transition zones, increasing knowledge of the geological setting and hydrogeological context of mountain springs is pivotal for their preservation and optimized exploitation. However, the complexity and remoteness of mountain waters make them difficult to conceptualize and analyse, both observationally and instrumentally. In this context, using detailed geological mapping and hydrogeological surveys, geophysical data can provide useful information on the subsurface setting. Electrical resistivity tomography (ERT) surveys are utilized in this work for the investigation of the Montellina Spring (MS), which is located in the low Dora Baltea Valley and represents a significant drinking water source in the alpine context. Geophysical surveys, complemented by specific geological and hydrogeological observations, allowed a detailed reconstruction of the water circuit that supplies the spring along an articulated buried glacial valley and a loose bedrock in a DSGSD (deep-seated gravitational slope deformation) environment. The methodological approach also provides the basis for its successful application in similar geological contexts. Full article

Autonomous driving technology is developing rapidly, particularly in vision-based approaches that rely on cameras to monitor the environment. However, one of the critical challenges for autonomous vehicles is the ability to adapt to different weather conditions, as environmental factors such as clouds, fog, rain, sand, shine, snow, and the sunrise significantly impact their perceptual capabilities. Control strategies of the vehicles must be dynamically adjusted based on real-time weather conditions to ensure safe and efficient driving. For the strategies, we developed a novel weather perception model to improve the adaptability of autonomous driving systems. The model is more lightweight than an existing study, as it is computationally efficient with enhanced performance. Moreover, the model detects a weather type, improving its robustness, and providing reliable weather awareness for autonomous driving. Full article

Localizing ground-penetrating radar (LGPR) benefits from deep subsurface coupling, ensuring robustness against surface variations and adverse weather. While LGPR is widely recognized as the complement of existing vehicle localization methods, its reliance on prior maps introduces significant challenges. Channel misalignment during traversal positioning and time-dimension distortion caused by non-uniform platform motion degrade matching accuracy. Furthermore, rain and snow conditions induce subsurface water-content variations that distort ground-penetrating radar (GPR) echoes, further complicating the localization process. To address these issues, we propose a weather-resilient adaptive spatio-temporal mask alignment algorithm for LGPR. The method employs adaptive alignment and dynamic time warping (DTW) strategies to sequentially resolve channel and time-dimension misalignments in GPR sequences, followed by calibration of GPR query sequences. Moreover, a multi-level discrete wavelet transform (MDWT) module enhances low-frequency GPR features while adaptive alignment along the channel dimension refines the signals and significantly improves localization accuracy under rain or snow. Additionally, a local matching DTW algorithm is introduced to perform robust temporal image-sequence alignment. Extensive experiments were conducted on both public LGPR datasets: GROUNDED and self-collected data covering five challenging scenarios. The results demonstrate superior localization accuracy and robustness compared to existing methods. Full article

UAV-based inspections are an effective way to ensure railway safety and have gained significant attention. However, images captured during complex weather conditions, such as rain, snow, or fog, often suffer from severe degradation, affecting image recognition accuracy. Existing algorithms for removing rain, snow, and fog have two main limitations: they do not adaptively learn features under varying weather complexities and struggle with managing complex noise patterns in drone inspections, leading to incomplete noise removal. To address these challenges, this study proposes a novel framework for removing rain, snow, and fog from drone images, called WeatherClean. This framework introduces a Weather Complexity Adjustment Factor (WCAF) in a parameterized adjustable network architecture to process weather degradation of varying degrees adaptively. It also employs a hierarchical multi-scale cropping strategy to enhance the recovery of fine noise and edge structures. Additionally, it incorporates a degradation synthesis method based on atmospheric scattering physical models to generate training samples that align with real-world weather patterns, thereby mitigating data scarcity issues. Experimental results show that WeatherClean outperforms existing methods by effectively removing noise particles while preserving image details. This advancement provides more reliable high-definition visual references for drone-based railway inspections, significantly enhancing inspection capabilities under complex weather conditions and ensuring the safety of railway operations. Full article

The conductors of high-speed railway OCSs (Overhead Contact Systems) are susceptible to conductor galloping due to the impact of natural elements such as strong winds, rain, and snow, resulting in conductor fatigue damage and significantly compromising train operational safety. Consequently, monitoring the galloping status of conductors is crucial, and instance segmentation techniques, by delineating the pixel-level contours of each conductor, can significantly aid in the identification and study of galloping phenomena. This work expands upon the YOLO11-seg model and introduces an instance segmentation approach for galloping video and image sensor data of OCS conductors. The algorithm, designed for the stripe-like distribution of OCS conductors in the data, employs four-direction Sobel filters to extract edge features in horizontal, vertical, and diagonal orientations. These features are subsequently integrated with the original convolutional branch to form the FDSE (Four Direction Sobel Enhancement) module. It integrates the ECA (Efficient Channel Attention) mechanism for the adaptive augmentation of conductor characteristics and utilizes the FL (Focal Loss) function to mitigate the class-imbalance issue between positive and negative samples, hence enhancing the model’s sensitivity to conductors. Consequently, segmentation outcomes from neighboring frames are utilized, and mask-difference analysis is performed to autonomously detect conductor galloping locations, emphasizing their contours for the clear depiction of galloping characteristics. Experimental results demonstrate that the enhanced YOLO11-seg model achieves 85.38% precision, 77.30% recall, 84.25% AP@0.5, 81.14% F1-score, and a real-time processing speed of 44.78 FPS. When combined with the galloping visualization module, it can issue real-time alerts of conductor galloping anomalies, providing robust technical support for railway OCS safety monitoring. Full article

Lidar technology is a key sensor for autonomous driving due to its precise environmental perception. However, adverse weather and atmospheric conditions involving fog, rain, snow, dust, and smog can impair lidar performance, leading to potential safety risks. This paper introduces a comprehensive methodology to simulate lidar systems under such conditions and validate the results against real-world experiments. Existing empirical models for the extinction and backscattering of laser beams are analyzed, and new models are proposed for dust storms and smog, derived using Mie theory. These models are implemented in the CARLA simulator and evaluated using Robot Operating System 2 (ROS 2). The simulation methodology introduced allowed the authors to set up test experiments replicating real-world conditions, to validate the models against real-world data available in the literature, and to predict the performance of the lidar system in all weather conditions. This approach enables the development of virtual test scenarios for corner cases representing rare weather conditions to improve robustness and safety in autonomous systems. Full article

Accurate weed detection in soybean fields is essential for enhancing crop yield and reducing herbicide usage. This study proposes a YOLO-SW model, an improved version of YOLOv8, to address the challenges of detecting weeds that are highly similar to the background in natural environments. The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. Additionally, a dataset of six common soybean weeds was expanded to 12,500 images through simulated fog, rain, and snow augmentation, effectively resolving data imbalance and boosting model robustness. The experimental results highlight both the technical superiority and practical relevance: YOLO-SW achieves 92.3% mAP@50 (3.8% higher than YOLOv8), with recognition accuracy and recall improvements of 4.2% and 3.9% respectively. Critically, on the NVIDIA Jetson AGX Orin platform, it delivers a real-time inference speed of 59 FPS, making it suitable for seamless deployment on intelligent weeding robots. This low-power, high-precision solution not only bridges the gap between deep learning and precision agriculture but also enables targeted herbicide application, directly contributing to sustainable farming practices and environmental protection. Full article

The spatiotemporal changes and driving mechanisms of rain-on-snow (ROS) events and their intensities are crucial for responding to disasters triggered by such events. However, there is currently a lack of detailed assessment of the seasonal variations and driving mechanisms of ROS events and their intensities in China. Therefore, this study utilized daily meteorological data and daily snow depth data from 513 stations in China during 1978–2020 to investigate spatiotemporal variations of ROS events and their intensities. Also, based on the detrend and partial correlation analysis model, the driving factors of ROS events and their intensity were explored. The results showed that ROS events primarily occurred in northern Xinjiang, the Qinghai–Tibet Plateau, Northeast China, and central and eastern China. ROS events frequently occurred in the middle and lower Yangtze River Plain in winter but were easily overlooked. The number and intensity of ROS events increased significantly (p < 0.05) in the Changbai Mountains in spring and the Altay Mountains and the southeast part of the Qinghai–Tibet Plateau in winter, leading to heightened ROS flood risks. However, the number and intensity of ROS events decreased significantly (p < 0.05) in the middle and lower Yangtze River Plain in winter. The driving mechanisms of the changes for ROS events and their intensities were different. Changes in the number of ROS events and their intensities in snow-rich regions were driven by rainfall days and quantity of rainfall, respectively. In regions with more rainfall, these changes were driven by snow cover days and snow water equivalent, respectively. Air temperature had no direct impact on ROS events and their intensities. These findings provide reliable evidence for responding to disasters and changes triggered by ROS events. Full article

Rivers play a crucial role in the hydrological cycle and serve as essential freshwater resources for both human populations and ecosystems. Climate change significantly alters precipitation patterns and river discharge variability. However, the impact of precipitation patterns (rainfall and snowfall) and air temperature on river discharge in coastal zones remains inadequately understood. This study focused on Toyama Prefecture, located along the Sea of Japan, as a representative coastal area. We analyzed over 30 years of datasets, including air temperature, precipitation, snowfall, and river discharge, to assess the effects of climate change on river discharge. Trends in hydroclimatic datasets were assessed using the rescaled adjusted partial sums (RAPS) method and the Mann–Kendall (MK) non-parametric test. Furthermore, a correlation analysis and the Structural Equation Model (SEM) were applied to construct a relationship between precipitation, temperature, and river discharge. Our findings indicated a significant increase in air temperature at a rate of 0.2 °C per decade, with notable warming observed in late winter (January and February) and early spring (March). The average river fluxes for the Jinzu, Oyabe, Kurobe, Shou, and Joganji rivers were 182.52 m³/s, 60.37 m³/s, 41.40 m³/s, 38.33 m³/s, and 18.72 m³/s, respectively. The tipping point for snowfall decline occurred in 1992, marked by an obvious decrease in snowfall depth. The SEM showed that, although rainfall dominated the changes in river discharge (loading = 0.94), the transition from solid (snow) to liquid (rain) precipitation may alter the river discharge regime. The percentage of flood occurrence increased from 19% (1940–1992) to 41% (1993–2020). These changes highlight the urgent need to raise awareness about the impacts of climate change on river floods and freshwater resources in global coastal regions. Full article

This article examines the modeling of sustainable development in transport logistics, focusing on the impact of climate factors, changing weather conditions, and digitalization processes. The study analyzes the complex influence of adverse weather phenomena, such as fog, rain, snow, extreme temperatures, and strong winds, whose frequency and intensity are increasing due to climate change, on the efficiency, safety, and reliability of transport systems across all modes except pipelines. Special attention is paid to the integration of weather-resilient sensor technologies, including LiDAR, thermal imaging, and advanced monitoring systems, to strengthen infrastructure resilience and ensure uninterrupted transport operations under environmental stress. The methodological framework combines comparative analytical methods with economic–mathematical modeling, particularly Leontief’s input–output model, to evaluate the mutual influence between the transport sector and sustainable economic growth within an interconnected ecosystem of economic and technological factors. The findings confirm that data-driven management strategies, the digital transformation of logistics, and the strengthening of centralized hubs contribute significantly to increasing the resilience and flexibility of transport systems, mitigating the negative economic impacts of climate risks, and promoting long-term sustainable development. Practical recommendations are proposed to optimize freight flows, adapt infrastructure to changing weather risks, and support the integration of innovative digital technologies as part of an evolving ecosystem. Full article

In this study, the relationship between radar reflectivity and rain rate (Z–R) was investigated. The analysis was conducted using data collected by the OTT Parsivel¹ disdrometer during the periods 2012–2014 and 2019–2025 in Warsaw, Poland. As a first step, the parameters a and b of the power-law Z–R relationship were estimated separately for three precipitation types: rain, sleet (rain with snow), and snow. Subsequently, observational data from all 12 months of the annual cycle were used to derive Z–R relationships for 118 individual precipitation events. To date, only a few studies of this kind have been conducted in Poland. In the analysis limited to rain events, the estimated parameters (a = 265, b = 1.48) showed relatively minor deviations from the classical Z–R function for convective rainfall, Z = 300R^1.4. However, the parameter a deviated more noticeably from the Z = 200R^1.6 relationship proposed by Marshall and Palmer, which is commonly used to convert radar reflectivity into rainfall estimates, including in the Polish POLRAD radar system. The dataset used in this study included rainfall events of varying types, both stratiform and convective, which contributed to the averaging of Z–R parameters. The values for the parameter a in the Z–R relationship estimated for the other two categories of precipitation types, sleet and snow, were significantly higher than those determined for rain events alone. The a values calculated for individual events demonstrated considerable variability, ranging from 80 to 751, while the b values presented a more predictable range, from 1.10 to 1.77. The highest parameter a values were observed during the summer months: June, July, and August. The variability in the Z–R relationship for individual events assessed in this study indicates the need for further research under diverse meteorological conditions, particularly for stratiform and convective precipitation. Full article

Warm conditions during typically cold winters impact runoff and resulting hydraulic processes in channels where ice-cover would typically dominate. This field study on a short, low-slope reach in Southern Ontario, Canada, examined hydrologic and hydraulic processes with a focus on winter runoff events and subsequent bed shear stress variability. Through winter 2024, six cross-sections over a ~100 m reach were monitored near-weekly to measure hydraulic geometry and velocity profiles. These data characterized channel processes and estimated bed shear stress with law of the wall. In this channel, velocity increased more rapidly than width or depth with rising discharge and influenced bed shear stress distribution. Bed shear stress magnitudes were highest (means ranged ~2–6 N/m²) and most variable over gravel beds compared to the exposed bedrock (means ranged ~0.05–2 N/m²). Through a rain-on-snow (ROS) event in late January, bed shear stress estimates decreased dramatically over the rougher gravel bed, despite minimal changes in water depth and velocity. Pebble counts before, during, and after the event, showed that the proportion of finer-sized particles (i.e., <5 cm) increased while median grain size did not vary. These observations align with findings from both flume and field studies and suggest that milder winters reduce gravel-bed roughness through finer-sized sediment deposition, altering sediment transport dynamics and affecting gravel habitat suitability. Additionally, limited ice-cover leads to lower bed shear stresses and thus finer-sized materials are deposited, further impacting gravel habitat suitability. Results highlight the importance of winter hydrologic variability in shaping channel processes and inform potential stream responses under future climate scenarios. Full article

The dynamic relationship between microplastics (MPs) in the air and on the Earth’s surface involves both natural and anthropogenic forces. MPs are transported from the ocean to the air by bubble scavenging and sea spray formation and are released from land sources by air movements and human activities. Up to 8.6 megatons of MPs per year have been estimated to be in air above the oceans. They are distributed by wind, water and fomites and returned to the Earth’s surface via rainfall and passive deposition, but can escape to the stratosphere, where they may exist for months. Anthropogenic sprays, such as paints, agrochemicals, personal care and cosmetic products, and domestic and industrial procedures (e.g., air conditioning, vacuuming and washing, waste disposal, manufacture of plastic-containing objects) add directly to the airborne MP load, which is higher in internal than external air. Atmospheric MPs are less researched than those on land and in water, but, in spite of the major problem of a lack of standard methods for determining MP levels, the clothing industry is commonly considered the main contributor to the external air pool, while furnishing fabrics, artificial ventilation devices and the presence and movement of human beings are the main source of indoor MPs. The majority of airborne plastic particles are fibers and fragments; air currents enable them to reach remote environments, potentially traveling thousands of kilometers through the air, before being deposited in various forms of precipitation (rain, snow or “dust”). The increasing preoccupation of the populace and greater attention being paid to industrial ecology may help to reduce the concentration and spread of MPs and nanoparticles (plastic particles of less than 100 nm) from domestic and industrial activities in the future. Full article

Glaciers worldwide are in retreat, and their meltwater can modulate mountain aquifers. We examined whether mass loss of the Juncal Norte Glacier (central Chile) has affected groundwater storage in the Juncal River basin between 1990 and 2022. Recession-curve modeling of daily streamflow shows no statistically significant trend in basin-scale groundwater reserves (τ = 0.06, p > 0.05). In contrast, glacier volume declined significantly (−3.8 hm³/yr, p < 0.05), and precipitation at the nearby Riecillos station fell sharply during the 2008–2017 megadrought (p < 0.05) but exhibited no significant change beforehand. Given the simultaneous decreases in meteoric inputs (rain + snow) and glacier mass, one would expect groundwater storage to decline; its observed stability therefore suggests that enhanced glacier-melt recharge may be temporarily offsetting drier conditions. Isotopic evidence from comparable Andean catchments supports such glacio-groundwater coupling, although time lags of months to years complicate detection with recession models alone. Hence, while our results do not yet demonstrate a direct glacier–groundwater link, they are consistent with the hypothesis that ongoing ice loss is buffering aquifer storage. Longer records and tracer studies are required to verify this mechanism and to inform sustainable water resources planning. Full article