Search

One of the important areas of soil erosion science is the development of soil conservation measures. The analysis of published works shows that in Russia most of the techniques and methods to control soil erosion were developed in the 1960s–1980s. In recent decades, interest in scientific and practical developments in this area has been declining. Published data on erosion control measures are poorly systematized, and their generally accepted classification is absent. The typification of erosion-control measures proposed by the authors made it possible to systematize all these measures found in the works and to create their register in the form of a table. Information about the efficiency of erosion control measures is scarce and contradictory. The results of agrotechnical measures are best studied. Non-inversive tillage is the most efficient among agrotechnical measures, it helps to reduce soil erosion by 70–90% according to published data; other agrotechnical measures, on average, reduce the rate of soil erosion by half. Climate change and socioeconomic transformation in the agricultural and industrial complex of Russia in recent decades have led to significant changes in the rate and volume of soil erosion. However, the analysis of literature indicates a shortage of works aimed at adapting existing erosion control measures, or developing new measures with due account for global and regional changes in erosion/deposition processes. [ABSTRACT FROM AUTHOR]

The polymer-facilitated flux of ice algae on Arctic shelves can initiate benthic activity and growth after the nutritionally constrained winter period. Lipid-rich ice algae are readily consumed by benthos and those entering the sediment can benefit deposit feeders. Ice algae assimilated by benthic organisms cascade up multiple trophic levels within the benthic sub-web, re-entering the pelagic sub web through habitat coupling species. Pelagic predators can have significant ice-algal carbon signals obtained from the benthic compartment. Sympagic-pelagic-benthic coupling on Arctic shelves is expected to weaken with ongoing sea-ice change. This review discusses the phenology, quantity, and quality of ice-algal contributions to coupling, linked to thinning snow and ice cover including multi-year ice replacement. Predicting future coupling between marine sub-webs requires focused research that considers trophic markers of multiple carbon sources. [ABSTRACT FROM AUTHOR]

Background and Aims Reduced snow cover and increased air temperature variability are predicted to expose overwintering herbaceous plants to more severe freezing in some northern temperate regions. Legumes are a key functional group that may exhibit lower freezing tolerance than other species in these regions, but this trend has been observed only for non-native legumes. Our aim was to confirm if this trend is restricted to non-native legumes or whether native legumes in these regions also exhibit low freezing tolerance. Methods First, we transplanted legumes (five non-native species and four native species) into either an old field (non-native) or a prairie (native) and used snow removal to expose the plots to increased soil freezing. Second, we grew plants in mesocosms (old field) and pots (prairie species) and exposed them in controlled environment chambers to a range of freezing treatments (control, 0, −5 or −10 °C) in winter or spring. We assessed freezing responses by comparing differences in biomass, cover and nodulation between freezing (or snow removal) treatments and controls. Key Results Among legume species, lower freezing tolerance was positively correlated with a lower proportion of nodulated plants and active nodules, and under controlled conditions, freezing-induced reductions in above-ground biomass were lower on average in native legumes than in non-native legumes. Nevertheless, both non-native and native legumes (except Desmodium canadense) exhibited greater reductions in biomass in response to increased freezing than their non-leguminous neighbours, both in controlled environments and in the field. Conclusions These results demonstrate that both native and non-native legumes exhibit low freezing tolerance relative to other herbaceous species in northern temperate plant communities. By reducing legume biomass and nodulation, increased soil freezing could reduce nitrogen inputs into these systems. [ABSTRACT FROM AUTHOR]

In the wake of snowfall, it is crucial to restore safe road passage promptly and efficiently. Considering the timeliness of melting operations, comprehensiveness of melting areas, safety of operating personnel, and rationality of the amount of snow-melting agent, this paper proposes a dynamic spraying approach of snow-melting agent based on snow-melting autonomous vehicle. Firstly, a dynamic calculation model is designed based on real-time recognition of ice or snow covered on the road, which uses the first law of thermodynamics to calculate the required amount of snow-melting agent for specific road segments, especially considering the surrounding thermal supply effect and snow-covering depth predicted by micro-regional weather forecasts within the expected operating period to adjust the amount. Secondly, it coordinates the spraying amount with the operating speed, achieving quantitative and uniform spraying of snow-melting agents on specific road segments. In addition, a novel form of snow removal based on autonomous driving is designed, which can move smoothly on particular road segments in multiple scenarios at a speed that matches the amount of snow-melting agent sprayed. Numerical experiments were conducted on the overall approach to verify its feasibility, adaptability, and economic and environmental sustainability in complex and extensive areas. The proposed approach is beneficial for exploring new fields of uncrewed operations. It promotes safe, efficient, and environmental clearance of ice-covered or snow-covered roads. [ABSTRACT FROM AUTHOR]

The accumulation of snow is one of the essential reasons for the significant decrease in the power generation efficiency of PV stations in winter. For this reason, this paper firstly provides a detailed review of the current research into the physical processes and shedding reasons for the natural melting of snow on PV module surfaces. Then, several common techniques to promote snow and ice melting on PV surfaces are discussed, and the feasibility and shortcoming of ultrasonic technology in removing high‐density snow and ice are analyzed in detail. Finally, this paper proposes two snow sensing technologies for PV modules, namely, image processing method and interdigital capacitance method. Among them, the image method based on fixed camera and UAV detection is more suitable for wide area PV power stations, while the interdigital capacitance method is helpful in obtaining snow characteristic parameters, such as icing type and LWC. The relevant research has certain reference significance for PV power stations to formulate a more reasonable snow removal plan and power generation forecast. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Addressing the challenge of ensuring robustness in vision‐based target recognition algorithms under adverse weather conditions, such as rain, snow, and fog, is crucial. In this paper, we introduce a novel approach for road target detection that can effectively operate under various weather conditions. Our method is based on the cascade task framework of target detection, complemented by image restoration techniques. Specifically, we have developed a denoising network tailored to meet the demands of de‐raining and snow removal tasks. This network leverages prior knowledge about the mask, enhancing its effectiveness. In real‐world scenarios featuring fog, wet conditions, and snow‐covered roads, our proposed method demonstrates a significant improvement in both recall rate and accuracy compared to conventional single‐object detection algorithms. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper is devoted to cloud and snow semantic segmentation using multispectral satellite imagery, received from a multizone scanning instrument (MSU-GS) installed on the Russian satellite Electro-L No. 2. For segmentation, some additional geographic information is used such as latitude, longitude, and altitude. The main difficulty of evaluating snow and cloud masks from MSU-GS data is absence of shortwave infrared (SWIR) spectral channels in the 1.3 - 1.6 μ m range, which are necessary for accurate snow and cloud distinction. This fact makes it impossible to apply any of existing algorithms for cloud and snow destinction used for other multispectral satellite imagery. The results of the presented work include a self-collected dataset, consisting of cloud masks from the meteorological satellites GOES-16 and Meteosat-10 L2 products, snow masks from Terra/MODIS products, considered to be the ground truth for multispectral imagery from Electro-L No. 2 with geographical information for each sample, as well as a trained Multi-Scale Attention Network (MANet), able to perform snow and cloud segmentation for multispectral data from Electro-L No. 2. The purpose of this study is to create a stable algorithm for MSU-GS imagery to conduct an operational snow and cloud coverage monitoring of the full Earth disk, that can not be evaluated by using multispectral data from other Russian satellites. The novelty of the work is the combining geographical and multispectral information with weighted sampling approach for hard samples, as well as using other satellite products for creating ground truth masks for needed satellite imagery. The developed algorithm was tested on various scenes during all seasons with F 1 and IoU metrics. We reach segmentation quality equal to F 1 = 0.7454 , F 1 = 0.8773 and IoU = 0.7398 , IoU = 0.7976 for snow and clouds respectively. The proposed neural network can be used at any time of year during daytime for processing Electro-L No. 2 multispectral data in real time. [ABSTRACT FROM AUTHOR]

The "bare runway" principle aims to ensure passenger and employee safety by making runways more usable during winter conditions, allowing for easier removal of contaminants like snow and ice. Maintaining runway operations in winter is essential, but it involves considerable cost and environmental impacts. Greater knowledge about the de-icing and anti-icing performance of runway de-icing products (RDPs) optimizes operations. The ice melting test, as per the AS6170 standard, gauges the rate at which an RDP dissolves an ice mass to determine RDP effectiveness. Here, we introduce a novel integrated methodology for assessing RDP-related ice melting. We combine laboratory-based procedures with infrared thermography and Raman spectroscopy to monitor the condition of RDPs placed on ice. The plateau of maximum efficiency, marked by the most significant Raman peak intensity, corresponds to the peak minimum temperature, indicating optimal RDP performance. Beyond this point, RDP efficacy declines, and the system temperature, including melted contaminants and RDP, approaches ambient temperature. Effective RDP performance persists when the ambient temperature exceeds the mixture's freezing point; otherwise, a freezing risk remains. The initial phases of RDP–ice contact involve exothermic reactions that generate brine, followed by heat exchange with surrounding ice to encourage melting. The final phase is complete ice melt, leaving only brine with reduced heat exchange on the surface. By quantifying these thermal and chemical changes, we gain a deeper understanding of RDP-related ice melting, and a more robust assessment can be provided to airports using RDPs. [ABSTRACT FROM AUTHOR]

This work examined the performance of self-heating concrete under laboratory thermal conditions and outdoor real-time conditions during the fall and winter seasons. Snow-melting and freeze–thaw performance of low-temperature phase change materials (PCM) incorporated self-heating concrete slabs in various scales were evaluated. PCM exhibited high enthalpy of fusion (ΔHf≈170–180 J/g), long-term thermal stability, and desirable supercooling. The experimental program included (1) optimization of concrete mix designs for maximum PCM incorporation, (2) characterization of thermal properties of PCM-mortar specimens using longitudinal guarded comparative calorimetry (LGCC), and (3) large-scale PCM concrete slabs in outdoor conditions to evaluate the real-time thermal performance against freeze–thaw events and snow-melting efficiency. Two different approaches were used to incorporate PCM in concrete: (1) submersion of liquid PCM in porous lightweight aggregates (PCM-LWA); and (2) microencapsulated PCM (MPCM). Both PCM-LWA and MPCM concrete not only exhibited promising snow-melting capabilities but also lowered the number of freeze–thaw cycles during cold seasons. PCM-LWA concrete performed better in decreasing the number of freeze–thaw (F-T) cycles due to the undercooling phenomenon created by the LWA pore network confinement pressure, allowing gradual latent heat release; the undercooling phenomenon in PCM-LWA results in phase transformation in a wider low-temperature range (i.e., 3.94°C to −13.04°C). Therefore, the PCM-LWA concrete was effective in melting snow within a wider range of low temperatures. MPCM concrete was found to provide a rapid melting capability during a snowfall event due to its "one-shot" heat release phenomenon. Both LWA-PCM and MPCM concrete slabs demonstrated promising heat response and snow-melting capability. Snowfall and freeze–thaw cycles occur frequently during winter seasons in North American regions with cold climate, resulting in snow accumulation on concrete roads and flatworks as well as concrete freeze–thaw damage. In this paper, a "self-heating" concrete was developed via incorporation of low-temperature phase change material (PCM), and its promising snow removal and freeze–thaw improvements were validated. The self-heating concrete can be used to construct pavements, driveways, bridge decks, and any other types of flatworks. When the ambient temperature falls to ∼0°C , PCM will release desirable amounts of heat energy (ΔHf=170–180 J per g of PCM added) by changing its phase from liquid to solid. As a result, the accumulated snow and ice melts at a gradual pace. In addition, heat release from the incorporated PCM lowers the number of freeze–thaw cycles, improving freeze–thaw performance of concrete made elements in cold regions, which in turn improves concrete durability and service life by minimizing the susceptibility to freeze–thaw scaling and spalling. [ABSTRACT FROM AUTHOR]

Snow and ice route removal activities play an essential role in travelers' safety and roadways' reliability. They often require the deployment of a large number of snowplow trucks on a vast maintenance area and involve a set of vehicle routing problems with multiple objectives and constraints. Many transportation agencies have limited resources and budgets for snow and ice route removal activities during winter maintenance operations. The objective of this paper is to propose a constraint-based snowplow optimization model to accommodate vehicle routing problems and justify the optimized fleet size for achieving the maximum level of service in snow and ice route removal. The proposed snowplow optimization model was developed using the geographic information system (GIS) base maps created by using ArcGIS Pro (version 2.7). After the model was tested and verified by experts in the Kansas Department of Transportation, it was applied to the snow and ice route removal activities in a district in Kansas as an illustrative example. The optimization results showed that the level of service could be increased up to 81% with an average route removal efficiency of 86% and the total travel time required to treat all snow routes in the selected district can be reduced by approximately 29 h for one treating iteration. The proposed model contributed to the winter operation maintenance body of knowledge by providing an efficient approach to snow and ice route removal activities. The findings of this study also may assist transportation agencies in optimizing their snow route removal practice in winter maintenance operations. This study developed a snowplow route optimization model using a geographic information system (GIS)-based software platform to enhance the efficiency of snow removal of Departments of Transportation of states in their winter maintenance. The model outcomes provided a detailed set of optimized snowplow route maps with time-related parameters (e.g., total travel time, plowing time, and deadhead time), snowplow route efficiency, and percentage of level of service (LOS) satisfaction of snow removal. Then, transportation agencies can utilize the optimized routing networks to monitor their resources spent on snow and ice control activities and increase the LOS in their maintenance operations. The results of this study enable transportation agencies to effectively justify the fleet size required at each service area and efficiently allocate limited resources to maintain all roadways in an economic, safety, and reliability manner. This study also facilitates the automation of snowplow optimization processes using GIS to provide satisfactory services during snow and ice events. The proposed model can serve as a tool to assist the agencies in directing any future facility locations and capacities for their winter maintenance. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image desnowing is a challenging task in computer vision, as it requires the removal of snow from images while preserving the underlying scene structure and content. In order to achieve high performance, desnowing methods need to be able to effectively capture both local and global information in the image. The proposed method addresses this challenge by introducing a novel Context-aware Feature Aggregation (CFA) module. The CFA module is designed to capture both local and global information by aggregating features of the network in latent space. This allows the method to better understand the contextual relationships in the image, which is essential for accurate snow removal. In addition to the CFA module, the proposed method also introduces a Selective Refinement Head (SRH). The SRH is designed to adaptively fuse coarse features from the encoder and decoder of the network. This allows the method to refine the output by incorporating relevant information from both low-level and high-level representations. Finally, the proposed method leverages the capabilities of contrastive learning to better align the desnow images and ground-truth images in perceptual space. This leads to improved image quality and desnowing performance. Extensive experiments on both synthetic and real-world datasets show that the proposed method achieves state-of-the-art results on image desnowing task. [ABSTRACT FROM AUTHOR]

Image restoration under adverse weather conditions poses a challenging task. Previous research efforts have predominantly focused on eliminating rain and fog phenomena from images. However, snow, being another common atmospheric occurrence, also significantly impacts advanced computer vision tasks such as object detection and semantic segmentation. Recently, there has been a surge of methods specifically targeting snow removal, with the majority employing visual Transformers as the backbone network to enhance restoration effectiveness. Nevertheless, due to the quadratic computations required by Transformers to model long-range dependencies, this significantly escalates the time and space consumption of deep learning models. To address this issue, this paper proposes an efficient snow removal Transformer with a global windowing network (SGNet). This method forgoes the localized windowing strategy of previous visual Transformers, opting instead to partition the image into multiple low-resolution subimages containing global information using wavelet sampling, thereby ensuring higher performance while reducing computational overhead. Extensive experimentation demonstrates that our approach achieves outstanding performance across a wide range of benchmark datasets and can rival methods employing CNNs in terms of computational cost. [ABSTRACT FROM AUTHOR]

Entanglement in commercial fishing gear is one of the main factors inhibiting the recovery of critically endangered North Atlantic right whales. Installing low-breaking-strength (LBS) components in the buoy lines and main lines of stationary fishing gear may be a viable solution for some fisheries. But is it an effective solution for deep-water trap fisheries? This study quantified in-line rope tensions observed during fishing operations for snow crab (Chionoecetes opilio) in Newfoundland and Labrador, Canada. We conducted a controlled fishing experiment in which we documented the loads experienced while hauling fleets of traps. The results showed that several factors contributed to the loads observed, including the components of the traps, the presence of crabs, and environmental conditions such as wind direction and wave height. According to the statistical models, the maximum tension from the estimated marginal means was 477.53 kgf in the buoy line and 987.99 kgf in the main line for the baited hauls, which exceeds the safe working load (154 kgf) of the proposed low-breaking-strength components. Our results suggest that LBS components are not a viable solution for this deep-water fishery. [ABSTRACT FROM AUTHOR]

This study focuses on temperature and snowfall conditions in Poland, both of which were analyzed from 1981 to 2020. A 40-year record of daily snow fraction time series values was reconstructed using a unique and global multi-source weighted-ensemble precipitation (MSWEP) product, which provided a spatially and temporally consistent reference for the assessment of meteorological conditions. The average states and trends in snow fraction and temperature were analyzed across several years, focusing on the 6-month cold season (November–April). The impact of temperature on the snow fraction pattern was assessed by introducing a snow fraction sensitivity index. To predict short-term changes in snow conditions, a proxy model was established; it incorporated historical trends in the snow fraction as well as its mean state. This study provides clear evidence that the snow fraction is principally controlled by increases in temperature. A warming climate will thus cause a decline in the snow fraction, as we observed in vast lowland areas. Given the ongoing global warming, by the 2050s, snow-dominated areas may go from covering 86% to only 30% of the country's surface; they will be converted into transient rain–snow areas. Our results demonstrate that a decline in snow water resources has already occurred, and these resources are expected to diminish further in the near future. New insights into the sensitivity of the snow fraction to climate warming will expand our collective knowledge of the magnitude and spatial extent of snow degradation. Such widespread changes have implications for the timing and availability of soil and groundwater resources as well as the timing and likelihood of floods and droughts. Thus, these findings will provide valuable information that can inform environmental managers of the importance of changing snowfall conditions, guiding them to include this aspect in future climate adaptation strategies. [ABSTRACT FROM AUTHOR]

Wind-blown snow is a frequent phenomenon in high-elevation and polar regions which impacts the surface energy and mass balance of these areas. Loose surface snow gets eroded and transported by wind which influences the snow particle's physical properties (size, shape, optical properties) that determine the characteristics of the emerging wind-impacted snowpack layer. During airborne snow transport, the governing processes are happening on the micro-scale, while the particles are transported over long distances. The unfolding processes and the evolution of the particle's physical properties are thus difficult to observe in-situ. Here we used cold-laboratory ring wind tunnel experiments as an interim solution to study the governing processes during airborne snow transport with stable water isotopes as tracers for these micro-scale processes. Repeated analysis of airborne-sampled snow by micro-computed tomography (μCT) documented a growing and rounding of snow particles with transport time with a concurrent decrease in specific surface area. Stable water isotope analysis of airborne snow and water vapour allowed us to attribute this evolution to the process of airborne snow metamorphism. The changes observed in the snow isotopic composition showed a clear isotopic signature of metamorphic deposition, which requires particle-air temperature gradients. These results question the validity of the thermal equilibrium assumption between particles and air inside the saltation layer of wind-blown snow events where the conditions are similar to the ones found in the wind tunnel. Our results thus refine the understanding of the governing processes in the saltation layer and suggest that the snow's isotopic composition can inform on local wind-blown snow events as the original snow isotope signal gets overprinted by airborne snow metamorphism. Thus, airborne snow metamorphism has the potential to influence the climate signal stored in snow and ice core stable water isotope records. [ABSTRACT FROM AUTHOR]

Growing evidence supports the hypothesis that temperate herbivores surf the green wave of emerging plants during spring migration. Despite the importance of autumn migration, few studies have conceptualized resource tracking of temperate herbivores during this critical season. We adapted the frost wave hypothesis (FWH), which posits that animals pace their autumn migration to reduce exposure to snow but increase acquisition of forage. We tested the FWH in a population of mule deer in Wyoming, USA by tracking the autumn migrations of n = 163 mule deer that moved 15–288 km from summer to winter range. Migrating deer experienced similar amounts of snow but 1.4–2.1 times more residual forage than if they had naïve knowledge of when or how fast to migrate. Importantly, deer balanced exposure to snow and forage in a spatial manner. At the fine scale, deer avoided snow near their mountainous summer ranges and became more risk prone to snow near winter range. Aligning with their higher tolerance of snow and lingering behavior to acquire residual forage, deer increased stopover use by 1 ± 1 day (95% CI) day for every 10% of their migration completed. Our findings support the prediction that mule deer pace their autumn migration with the onset of snow and residual forage, but refine the FWH to include movement behavior en route that is spatially dynamic. [ABSTRACT FROM AUTHOR]

In this study, a novel cambered snow removal device is designed to achieve automatic snow removal in large curved areas, such as the south roof of a Chinese solar greenhouse. The theory of structural parameters and shear force is ambiguous. People are not based on the greenhouse structure parameters for the selection of snow removal devices. Therefore, the quantitative relationship between the structure of the greenhouse span and the number of scissor arm-length knots is analysed, and the relationship between the material strength and application distance is determined. This study's objectives are (1) to establish a theoretical model of scissor arm motion and (2) to analyse the force distribution of the scissor arm using multi-body dynamics. The results show that the scissor arm of a round-arch greenhouse has fewer sections but a larger arm length, whereas the scissor arm of a traditional solar greenhouse has more sections but a smaller arm length. Based on the shear force of the scissor structure, the optimised wall thickness reduces the force of the node by 17%. [ABSTRACT FROM AUTHOR]

Copyright of Archives of Environmental Protection is the property of Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hydrogen (H2) is a promising low-carbon alternative to fossil fuels for many applications. However, significant gaps in our understanding of the atmospheric H2 budget limit our ability to predict the impacts of greater H2 usage. Here we use NOAA H 2 dry air mole fraction observations from air samples collected from ground-based and ship platforms during 2010–2019 to evaluate the representation of H 2 in the NOAA GFDL-AM4.1 atmospheric chemistry-climate model. We find that the base model configuration captures the observed interhemispheric gradient well but underestimates the surface concentration of H2 by about 10 ppb. Additionally, the model fails to reproduce the 1–2 ppb yr -1 mean increase in surface H 2 observed at background stations. We show that the cause is most likely an underestimation of current anthropogenic emissions, including potential leakages from H 2 -producing facilities. We also show that changes in soil moisture, soil temperature, and snow cover have most likely caused an increase in the magnitude of the soil sink, the most important removal mechanism for atmospheric H2 , especially in the Northern Hemisphere. However, there remains uncertainty due to fundamental gaps in our understanding of H 2 soil removal, such as the minimum moisture required for H 2 soil uptake, for which we performed extensive sensitivity analyses. Finally, we show that the observed meridional gradient of the H2 mixing ratio and its seasonality can provide important constraints to test and refine parameterizations of the H2 soil sink. [ABSTRACT FROM AUTHOR]

During snowfall, the utility of the road infrastructure is critical. Roads must be effectively cleared to ensure access to important locations and services. In this paper, we present an end-to-end framework for snow removal vehicle routing based on road priority. We offer an artificial intelligence-based image-based approach for estimating snow depth and traffic volume on roads. For segments monitored by CCTV cameras, we exploit images and supervised learning models to perform this task. For unmonitored roads, we use the Graph Convolutional Network architecture to predict parameters in a semi-supervised manner. Following that, we assign priority weights to all graph edges as a function of image-based attributes and road categories. We test the method using a real-world example, simulating snow removal within a study area in Montreal, Quebec, Canada. As input for the framework, we collect CCTV image data and combine it with a 2D map. As a result, more efficient snow removal operation can be achieved by optimizing the trajectories of trucks based on the computer vision module outputs.

Copyright of Japanese Journal of JSCE / Doboku Gakkai Ronbunshu is the property of Japan Society of Civil Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There has been an acceleration of groundwater loss in the Great Basin (GB) of the western U.S. as determined from total water storage (TWS) measurements from the GRACE/FO satellite missions. From 2002 to 2023, there was a loss of TWS in the GB of ∼68.7 km3 which is more than six times the current volume of the Lake Mead Reservoir. In this arid/semi‐arid region, groundwater is the primary factor contributing to the decade‐scale decline in TWS. Stronger declining trends are found in the western versus the eastern GB. Snow loading is the major cause of seasonal fluctuations of TWS in the GB. Despite annual replenishment of groundwater by snow, the downward trend persists even in notable snow years. Likely causes include declining snow mass, upstream water diversions and increased evaporation/sublimation due to increasing temperatures. Dire consequences for humans and wildlife are associated with this large loss of groundwater. Plain Language Summary: The 21st Century megadrought in the southwestern U.S. caused a dramatic acceleration of groundwater loss in the Great Basin (GB) of the western U.S. as determined from changes in the Earth's gravity measured by the GRACE satellites. Groundwater is a major component of total water storage (TWS) in the GB, but snowfall and snowmelt are the major causes of seasonal fluctuations of TWS. As a snowpack accumulates or melts, water is redistributed causing a rapid regional change in gravity that can be measured from space. From 2002 to 2023, there is a substantial loss of groundwater in the GB of ∼68.7 km3 which is more than six times the current volume of water in the Lake Mead Reservoir in Arizona/Nevada. Stronger declining trends of groundwater loss are found in the western part of the GB while weaker declining trends are found in the eastern part. Despite annual replenishment by snowfall, even in notable snow years like 2010–2011, 2016–2017, 2018–2019, and 2022–2023, the downward trend of groundwater depletion persists. Likely causes for this decline include declining snow mass, upstream water diversions and increased evaporation/sublimation due to increasing air and surface temperatures. Groundwater depletion is associated with dire consequences for humans and wildlife. Key Points: Snow accumulation in the Great Basin (GB) triggers an increase in total water storage (TWS) while snow ablation triggers a drop in TWSThere is an 68.7 km3 loss of groundwater in the GB from 2002 to 2023 which is more than six times greater than the current volume of Lake MeadThe 2002–2023 TWS decline in the GB is more pronounced in the western GB than in the eastern GB [ABSTRACT FROM AUTHOR]

Soil nitrogen (N) availability is one of the limiting factors of crop productivity, and it is strongly influenced by global change and agricultural management practices. However, very few studies have assessed how the winter drought affected soil N availability during the subsequent growing season under chemical fertilization. We conducted a field investigation involving snow removal to simulate winter drought conditions in a Mollisol cropland in Northeast China as part of a 6-year fertilization experiment, and we examined soil physicochemical properties, microbial characteristics, and N availability. Our results demonstrated that chemical fertilization significantly increased soil ammonium and total N availability by 42.9 and 90.3%, respectively; a combined winter drought and fertilization treatment exhibited the highest soil N availability at the end of the growing season. As the growing season continued, the variation in soil N availability was explained more by fertilization than by winter drought. The Mantel test further indicated that soil Olsen-P content and microbial carbon use efficiency (CUE) were significantly related to soil ammonium availability. A microbial community structure explained the largest fraction of the variation in soil nitrate availability. Microbial CUE showed the strongest correlation with soil N availability, followed by soil available C:P and bacteria:fungi ratios under winter drought and chemical fertilization conditions. Overall, we clarified that, despite the weak effect of the winter drought on soil N availability, it cannot be ignored. Our study also identified the important role of soil microorganisms in soil N transformations, even in seasonally snow-covered northern croplands. [ABSTRACT FROM AUTHOR]

Snow plays a crucial role in the global water cycle, providing water to over 20% of the world's population and serving as a vital component for flora, fauna, and climate regulation. Changes in snow patterns due to global warming have far-reaching impacts on water management, agriculture, and other economic sectors such as winter tourism. Additionally, they have implications for environmental stability, prompting migration and cultural shifts in snow-dependent communities. Accurate information on snow and its variables is, thus, essential for both scientific understanding and societal planning. This review explores the potential of remote sensing in monitoring snow water equivalent (SWE) on a large scale, analyzing 164 selected publications from 2000 to 2023. Categorized by methodology and content, the analysis reveals a growing interest in the topic, with a concentration of research in North America and China. Methodologically, there is a shift from passive microwave (PMW) inversion algorithms to artificial intelligence (AI), particularly the Random Forest (RF) and neural network (NN) approaches. A majority of studies integrate PMW data with auxiliary information, focusing thematically on remote sensing and snow research, with limited incorporation into broader environmental contexts. Long-term studies (>30 years) suggest a general decrease in SWE in the Northern Hemisphere, though regional and seasonal variations exist. Finally, the review suggests potential future SWE research directions such as addressing PMW data issues, downsampling for detailed analyses, conducting interdisciplinary studies, and incorporating forecasting to enable more widespread applications. [ABSTRACT FROM AUTHOR]

Background and aims: Cover crops can increase nitrogen (N) retention in agroecosystems by taking up soil soluble N when the grain crop is absent. We examined how the combination of cover crops and variability in winter conditions can affect soil N retention and N transfer to the subsequent crop. Methods: We used 15N tracer to quantify how the presence of cover crops (both winter-hardy and winter-killed) modifies the recovery by a corn crop of soil soluble N added the previous fall, and we used snow removal to assess how increased freezing would alter 15N recovery. We predicted snow removal would decrease 15N recovery in corn, and this decrease would be highest for plots with winter-hardy cover crops, given they remain vulnerable to increased frost over winter. Results: Cover crops approximately doubled corn grain 15N recovery (up to 8% of the added 15N) relative to the treatment that lacked cover crops (4% of the added 15N). Snow removal reduced 15N recovery in corn grain, but this effect did not differ among cover crop treatments. 15N recovery in soil at the time of corn harvest was increased by cover crops, with retention as high as 40% of the added 15N tracer, compared to only 20% for soil in the control plots. Soil 15N recovery was not significantly reduced by snow removal. Conclusion: Although increased soil freezing reduced grain N recovery, cover crops increased soil N retention, which indicates decreased N losses to the surrounding environment, and the potential for increased contributions to grain N in future years. [ABSTRACT FROM AUTHOR]

The seasonal coupling of plant and soil microbial nutrient demands is crucial for efficient ecosystem nutrient cycling and plant production, especially in strongly seasonal alpine ecosystems. Yet, how these seasonal nutrient cycling processes are modified by climate change and what the consequences are for nutrient loss and retention in alpine ecosystems remain unclear. Here, we explored how two pervasive climate change factors, reduced snow cover and shrub expansion, interactively modify the seasonal coupling of plant and soil microbial nitrogen (N) cycling in alpine grasslands, which are warming at double the rate of the global average. We found that the combination of reduced snow cover and shrub expansion disrupted the seasonal coupling of plant and soil N‐cycling, with pronounced effects in spring (shortly after snow melt) and autumn (at the onset of plant senescence). In combination, both climate change factors decreased plant organic N‐uptake by 70% and 82%, soil microbial biomass N by 19% and 38% and increased soil denitrifier abundances by 253% and 136% in spring and autumn, respectively. Shrub expansion also individually modified the seasonality of soil microbial community composition and stoichiometry towards more N‐limited conditions and slower nutrient cycling in spring and autumn. In winter, snow removal markedly reduced the fungal:bacterial biomass ratio, soil N pools and shifted bacterial community composition. Taken together, our findings suggest that interactions between climate change factors can disrupt the temporal coupling of plant and soil microbial N‐cycling processes in alpine grasslands. This could diminish the capacity of these globally widespread alpine ecosystems to retain N and support plant productivity under future climate change. [ABSTRACT FROM AUTHOR]

As a major real‐world problem, snow plowing has been studied extensively. However, most studies focus on deterministic settings with little urgency yet enough time to plan. In contrast, we assume a severe snowstorm with little known data and little time to plan. We introduce a novel time‐dependent multi‐visit dynamic safe street snow plowing problem and formulate it on a rolling‐horizon‐basis. To solve this problem, we develop an adaptive large neighborhood search as the underlying method and validate its efficacy on team orienteering arc routing problem benchmark instances. We create real‐world‐based instances for the city of Vienna and examine the effect of (i) different snowstorm movements, (ii) having perfect information, and (iii) different information‐updating intervals and look‐aheads for the rolling horizon method. Our findings show that different snowstorm movements have no significant effect on the choice of rolling horizon settings. They also indicate that (i) larger updating intervals are beneficial, if prediction errors are low, and (ii) larger look‐aheads are better suited for larger updating intervals and vice versa. However, we observe that less look‐ahead is needed when prediction errors are low. [ABSTRACT FROM AUTHOR]

Background: Recent climate changes have produced extreme climate events. This study focused on extreme snowfall and intended to discuss the vulnerability of temperate mammals against it through interspecies comparisons of spatial niches in northern Japan. We constructed niche models for seven non-hibernating species through wide-scaled snow tracking on skis, whose total survey length was 1144 km. Results: We detected a low correlation (rs < 0.4) between most pairs of species niches, indicating that most species possessed different overwintering tactics. A morphological advantage in locomotion cost on snow did not always expand niche breadth. In contrast, a spatial niche could respond to (1) drastic landscape change by a diminishing understory due to snow, possibly leading to changes in predator-prey interactions, and (2) the mass of cold air, affecting thermoregulatory cost and food accessibility. When extraordinary snowfall occurred, the nonarboreal species with larger body sizes could niche shift, whereas the smaller-sized or semi-arboreal mammals did not. In addition, compared to omnivores, herbivores were prone to severe restriction of niche breadth due to a reduction in food accessibility under extreme climates. Conclusions: Dietary habits and body size could determine the redundancy of niche width, which may govern robustness/vulnerability to extreme snowfall events. [ABSTRACT FROM AUTHOR]

Wind-induced snow transport has a strong influence on snow spatial variability, especially at spatial scales between 1 and 500 m in alpine environments. Thus, the evolution of operational snow modelling systems towards 100–500 m resolutions requires representing this process at these resolutions over large domains and entire snow seasons. We developed SnowPappus, a parsimonious blowing-snow model coupled to the state-of-the-art Crocus snow model able to cope with these requirements. SnowPappus simulates blowing-snow occurrence, horizontal transport flux and sublimation rate at each grid cell as a function of 2D atmospheric forcing and snow surface properties. Then, it computes a mass balance using an upwind scheme to provide eroded or accumulated snow amounts to Crocus. Parameterizations used to represent the different processes are described in detail and discussed against existing literature. A point-scale evaluation of blowing-snow fluxes was conducted, mainly at the Col du Lac Blanc observatory in the French Alps. Evaluations showed that SnowPappus performs as well as the currently operational scheme SYTRON in terms of blowing-snow occurrence detection, while the latter does not give access to spatialized information. Evaluation of the simulated suspension fluxes highlighted a strong sensitivity to the suspended particle's terminal fall speed. Proper calibrations allow the model to reproduce the correct order of magnitude of the mass flux in the suspension layer. Numerical performances of gridded simulations of Crocus coupled with SnowPappus were assessed, showing the feasibility of using it for operational snow forecast at the scale of the entire French Alps. [ABSTRACT FROM AUTHOR]

Realistic simulation of dynamic interaction between plants and different natural environmental factors is a key topic in the field of natural landscape simulation. At present, few researches focus on the accumulation of snow on plants and the simulation of the dynamic deformation of a branch under the influence of the accumulation of snow. Therefore, we propose a simulation method for the dynamic interaction between snowfall and plants based on physical models. We first analyze the structure of the target plant and establish its physical structure model. Then we use the particle system and the plant branch collision detection method to calculate the amount of snow accumulation on the branches and generate the fluctuating snow grid surface based on the Berlin noise function. Finally, the cantilever beam model is used to calculate the deformation of branches under snow pressure. This modeling method proposed in this paper can provide a new research idea and technical reference for simulating the dynamic interaction between plants and environmental factors represented by snow. [ABSTRACT FROM AUTHOR]

Improving underwater image quality is crucial for marine detection applications. However, in the marine environment, captured images are often affected by various degradation factors due to the complexity of underwater conditions. In addition to common color distortions, marine snow noise in underwater images is also a significant issue. The backscatter of artificial light on marine snow generates specks in images, thereby affecting image quality, scene perception, and subsequently impacting downstream tasks such as target detection and segmentation. Addressing the issues caused by marine snow noise, we have designed a new network structure. In this work, a novel skip-connection structure called a dual channel multi-scale feature transmitter (DCMFT) is implemented to reduce information loss during downsampling in the feature encoding and decoding section. Additionally, in the feature transfer process for each stage, iterative attentional feature fusion (iAFF) modules are inserted to fully utilize marine snow features extracted at different stages. Finally, to further optimize the network's performance, we incorporate the multi-scale structural similarity index (MS-SSIM) into the loss function to ensure more effective convergence during training. Through experiments conducted on the Marine Snow Removal Benchmark (MSRB) dataset with an augmented sample size, our method has achieved significant results. The experimental results demonstrate that our approach excels in removing marine snow noise, with a peak signal-to-noise ratio reaching 38.9251 dB, significantly outperforming existing methods. [ABSTRACT FROM AUTHOR]

Below-cloud scavenging (BCS) is the process of aerosol removal from the atmosphere between cloud base and the ground by precipitation (e.g., rain or snow), and affects aerosol number or mass concentrations, size distribution, and lifetime. An accurate representation of precipitation phases is important in treating BCS as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. The impact of different representations of BCS on existing model biases was examined through implementing a new aerosol BCS scheme in the Environment and Climate Change Canada (ECCC) air quality prediction model GEM-MACH and comparing it with the existing scavenging scheme in the model. Further, the current GEM-MACH employs a single-phase precipitation for BCS: total precipitation is treated as either liquid or solid depending on a fixed environment temperature threshold. Here, we consider co-existing liquid and solid precipitation phases as they are predicted by the GEM microphysics. GEM-MACH simulations, in a local-area domain over the Athabasca oil sands areas, Canada, are compared with observed precipitation samples, with a focus on the particulate base cation NH 4+ ; acidic anions NO 3- , SO 4= , HSO 3- in precipitation; and observed ambient particulate sulfate, ammonium, and nitrate concentrations. Overall, the introduction of the multi-phase approach and the new scavenging scheme enhances GEM-MACH performance compared to previous methods. Including a multi-phase approach leads to altered SO 4= scavenging and impacts the BCS of SO 2 into the aqueous phase over the domain. Sulfate biases improved from +46% to -5% relative to Alberta Precipitation Quality Monitoring Program wet sulfate observations. At Canadian Air and Precipitation Monitoring Network stations the biases became more negative, from -10% to -30% for the tests carried out here. These improvements contrast with prior annual average biases of +200% for SO 4= , indicating enhanced model performance. Improvements in model performance (via scores for correlation coefficient, normalized mean bias, and/or fractional number of model values within a factor of 2 of observations) could also be seen between the base case and the two simulations based on multi-phase partitioning for NO 3- , NH 4+ , and SO 4=. Whether or not these improvements corresponded to increases or decreases in NO 3- and NH 4+ wet deposition varied over the simulation region. The changes were episodic in nature – the most significant changes in wet deposition were likely at specific geographic locations and represent specific cloud precipitation events. The changes in wet scavenging resulted in a higher formation rate and larger concentrations of atmospheric particle sulfate. [ABSTRACT FROM AUTHOR]

Under‐ice photoautotrophs in lakes are generally considered to be limited by light rather than nutrients. Despite reduced light intensity under the ice, there is increasing evidence that suggests some lakes support high levels of photoautotrophs. We explored how snow cover (i.e., light) and nutrients (i.e., nitrogen and phosphorus) influence ice‐associated photoautotroph growth in a Minnesota, USA lake. Using a novel under‐ice approach, we deployed nutrient diffusing substrates (single or combined nutrient amendments) under two different light scenarios (snow covered, reduced light; snow removed, increased light) near the water‐ice interface to mimic a range of conditions ice‐associated photoautotrophs may be exposed to. Natural snow cover reduced light compared with snow removal, particularly early in the experiment before snow began to melt. When comparing photoautotroph chlorophyll a (Chl a) between snow treatments, we found a significant snow effect with higher concentrations in the snow removed treatment. We also found a significant nutrient effect, for all nutrient treatments, on Chl a concentrations in both snow conditions. The effect of any nutrient treatment on Chl a concentrations was similar. Our results suggest that ice‐associated photoautotrophs were able to grow in all snow conditions, but snow removal resulted in higher growth and nutrient availability also mediated responses. Thus, both light and nutrient conditions in the winter may strongly affect ice‐associated photoautotroph dynamics. [ABSTRACT FROM AUTHOR]

Climate change is leading to a less consistent snowpack in North America, and many species are affected by its loss. The question addressed in this study is how changes in temperature and moisture from the loss of snowpack separately affect overwintering fronds of the wintergreen fern Dryopteris intermedia (Muhl. ex Willd.) A. Gray. In a field study, the control had the snowpack left intact, the snow removal treatment had snow removed for the first and last 4 wk of winter, and the snow removal with barrier matched the snow removal treatment but with a moisture barrier over the plant. The control treatment had the most snow and the highest litter temperatures. Litter moisture was highest in the control and lowest in the snow removal treatment. Frond moisture was lower and frond damage was higher in the snow removal treatment than in the control and snow removal-with-barrier treatments. Frond damage was associated with the moisture but not the temperature in the environment around the frond. The loss of moisture can be as important as the lack of thermal insulation to damage experienced by D. intermedia when a snowpack is not present in winter. [ABSTRACT FROM AUTHOR]

This study addresses the critical issue of traffic safety in winter, particularly focusing on the challenges posed by ice and snow on roads. Traditional methods of snow and ice removal are often labor-intensive, inefficient, and environmentally harmful. The objective is to develop a more effective solution for asphalt pavement deicing. Inspired by the anti-icing coating technology used in high-voltage conductors, this research develops an ice-suppressing material designed to reduce the adhesion between snow, ice, and pavement surfaces. The material's performance is evaluated in terms of deicing efficiency, durability, adhesive properties, and its impact on pavement performance. Test results demonstrate that the developed ice-suppressing material significantly reduces the adhesion between the ice layer and the pavement, facilitating easier removal. This study concludes that the developed ice-suppressing material significantly enhances deicing efficiency on asphalt pavements. It exhibits strong hydrophobic properties, as evidenced by increased water droplet contact angles on coated surfaces (99.5° to 83.3°) compared to clean glass slides (39.2° to 29°). This hydrophobicity effectively reduces ice adhesion, decreasing tensile and shear strength of the ice layer by 38.2% and 63.6%, respectively. Additionally, the material demonstrates superior ice-melting capabilities in sub-zero temperatures, with coated ice cubes showing a higher mass reduction rate than uncoated ones. Importantly, its slow-release nature ensures sustained deicing performance over multiple cycles, maintaining effectiveness after seven test cycles. This study introduces an innovative ice-suppressing material that not only improves the efficiency and environmental impact of deicing methods but also contributes to enhancing road safety in winter conditions. The material's novel composition and sustained effectiveness present a significant advancement in the field of winter road maintenance. [ABSTRACT FROM AUTHOR]

In order to solve the problem of frequent snow and ice removal in winter, a micro-surfacing pavement with active snow and ice removal and slow-release function is developed. The snow-melting agent with sodium acetate, calcium acetate, and potassium acetate as the main components was prepared, and the optimum proportion was determined. With the help of hygroscopicity and oil absorption tests, the optimum surface modifier and its optimum concentration were determined. The microstructure of the snowmelt agent were characterized by infrared spectroscopy and scanning electron microscopy. The influence of snow-melting agent on the road performance was explored. The results show that the optimum proportion of sodium acetate, calcium acetate and potassium acetate is 37, 15 and 48 % respectiveiy. When the surface modifier: hydrogen silicone oil and KH560 solution concentration was 4.0% and 1.0% respectively, the slow-release effect was the optimum. The wear resistance and high temperature stability of micro-surfacing with the snowmelt agent were increased by 2.5 % and 8.2 %, respectively. It is most reasonable to determine that the optimal replacement amount of mineral powder is 50%. It is necessary to study the distribution of self-made snowmelt agent in the mixture in the future. [ABSTRACT FROM AUTHOR]

The accumulation of snow and ice formation in asphalt pavement riding surfaces plays a critical role in the direct contact of the roadway with vehicle traffic. Considering the importance of this matter to vehicle transportation safety and other pavement performance concerns, Phase Change Materials (PCMs) have emerged as a potential solution to make snow and ice removal more manageable. PCMs are substances that absorb and release thermal energy during the process of melting and freezing. There has been substantial research undertaken on the formulation of PCMs for pavement engineering applications. However, the potential benefits of their incorporation into asphalt pavements under actual thermal cycles and snowfall events have yet to be understood. This investigation examined the thermal performance of asphalt pavement slabs containing ideal PCMs to prevent freezing temperatures in the pavement structure and assist in melting snow and ice from the pavement's surface. The experimental and computational results demonstrate that, indeed, PCMs can enhance the thermal response of asphalt pavements to adverse environmental conditions. Overall, this study's findings support the use of PCMs in asphalt pavements to provide them with thermal energy storage properties for better low-temperature management and in-service performance. [ABSTRACT FROM AUTHOR]

Colour polymorphism can be maintained by colour morph‐specific benefits across environmental conditions. Currently, the amount and the duration of snow cover during winter decrease especially in northern latitudes, which can alter the potential for camouflage of animals with light and dark morphs. Tawny owls, Strix aluco, are colour‐polymorphic avian predators with dark (brown) and light (grey) colour morphs, where the grey morph is presumed to enjoy camouflage benefits under snowy conditions. We studied the camouflage potential of morphs in two tawny owls potential using passerines' probability to mob in the wild during winter with and without snow. For comparison with other seasons, we also repeated the experiment during spring and autumn. We found that grey tawny owls have a lower probability of being mobbed than the brown tawny owls only during snowy winters. The two colour morphs therefore experience differential benefits across snow conditions, which may help to maintain colour morphs in the population, although further warming of winter climate will reduce the potential for camouflage for grey tawny owls in northern latitudes. [ABSTRACT FROM AUTHOR]

Electrically-conductive concrete (ECON) can generate heat for melting pavement ice if an electrical voltage is applied, and this ECON-heated pavement system (HPS) is a viable alternative to the traditional snow removal methods. This study aimed at determining mixture proportions and procedures suitable for ready-mix concrete-plant-produced carbon fibre (CF)-reinforced ECON. Trial batches ECON production involving five different mixture proportions and procedures were performed at a ready-mix concrete plant. Study results suggest that CF begins to degrade with increased mixing time. A twelve-minute mixing time from the start of loading CF onto the mixer until the end produced the best-performing ECON, with an electrical resistivity of 224 Ω-cm and a heat-generation rate of 2°C/min. Scanning electron microscopic (SEM) image analysis shows that CF remained as blobs of fibre instead of being uniformly dispersed. Further research is recommended to find an efficient way of separating individual CF from the blobs and ensuring uniform CF dispersion to reduce the dosage rate and construction cost of ECON. [ABSTRACT FROM AUTHOR]

The phase in which precipitation falls—rainfall, snowfall, or sleet—has a considerable impact on hydrology and surface runoff. However, many weather stations only provide information on the total amount of precipitation, at other stations series are short or incomplete. To address this issue, data from 40 meteorological stations in Poland spanning the years 1966–2020 were utilized in this study to classify precipitation. Three methods were used to differentiate between rainfall and snowfall: machine learning (i.e., Random Forest), daily mean threshold air temperature, and daily wet bulb threshold temperature. The key findings of this study are: (i) the Random Forest (RF) method demonstrated the highest accuracy in rainfall/snowfall classification among the used approaches, which spanned from 0.90 to 1.00 across all stations and months; (ii) the classification accuracy provided by the mean wet bulb temperature and daily mean threshold air temperature approaches were quite similar, which spanned from 0.86 to 1.00 across all stations and months; (iii) Values of optimized mean threshold temperature and optimized wet bulb threshold temperature were determined for each of the 40 meteorological stations; (iv) the inclusion of water vapor pressure has a noteworthy impact on the RF classification model, and the removal of mean wet bulb temperature from the input data set leads to an improvement in the classification accuracy of the RF model. Future research should be conducted to explore the variations in the effectiveness of precipitation classification for each station. [ABSTRACT FROM AUTHOR]

With the increasing popularity of artificial intelligence applications,machine learning is also playing an increasingly important role in the Internet of Things (IoT) and the Internet of Vehicles (IoV). As an essential part of the IoV, smart transportation relies heavily on information obtained from images. However, inclement weather, such as snowy weather, negatively impacts the process and can hinder the regular operation of imaging equipment and the acquisition of conventional image information. Not only that, but the snow also makes intelligent transportation systems make the wrong judgment of road conditions and the entire system of the Internet of Vehicles adverse. This paper describes the single image snow removal task and the use of a vision transformer to generate adversarial networks. The residual structure is used in the algorithm, and the Transformer structure is used in the network structure of the generator in the generative adversarial networks, which improves the accuracy of the snow removal task. Moreover, the vision transformer has good scalability and versatility for larger models and has a more vital fitting ability than the previously popular convolutional neural networks. The Snow100K dataset is used for training, testing and comparison, and the peak signal-to-noise ratio and structural similarity are used as evaluation indicators. The experimental results show that the improved snow removal algorithm performs well and can obtain high-quality snow removal images. [ABSTRACT FROM AUTHOR]

Snow algae contribute to snowmelt by darkening the surface, reducing its albedo. However, the potential consequences of algae under the surface (such as after a fresh snowfall) on albedo reduction is not known. In this study, we examined the impact of sub-surface snow algae on surface energy absorption. The results indicate energy absorption across all analysed wavelength ranges when snow algae are snow-covered, an effect that was correlated with both cell densities and chlorophyll-a concentrations. These findings suggest that snow algae lower albedo and thus increase snow melt even when snow-covered. [ABSTRACT FROM AUTHOR]

The effect of snowfall on an image is not only the interference of snow particles but also snow streaks and masking effects (similar to haze). Snowy weather severely reduces the accuracy of computer vision systems. There is a lot of interest in how to effectively remove snow while preserving as much of the original image information as possible. Based on this, the authors propose an effective Generative Adversarial Network (GAN) snow removal algorithm for single images to solve the snow removal failure problem caused by irregular snow particles and snow streaks. Specifically, the authors improve the original GAN network as follows: A novel Transformer module, the Contextual Transformer (CoT) module, is adopted in the residual modules based generator. It effectively uses the contextual information of the snow streaks neighbourhood to restore the texture and information in the noisy image as much as possible based on the focus on snow streaks features. Also, using learnable Regionalized Normalization (RN‐L), potentially corrupted and undamaged regions are automatically detected for separate normalization, and global affine transformations are performed to enhance their fusion. In addition, a multi‐scale discriminator is used in the discriminator to make the discrimination more adequate and retain more details. Extensive experiments have shown that the authors' GAN network snow removal algorithm outperforms various current networks on snow removal studies in terms of evaluation metrics on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]