Search Results (525)

Autonomous driving systems encounter various uncertainties in real-world environments, many of which are difficult to represent in maps. Among them, accumulated snow poses a unique challenge since its shape and volume gradually change over time. If accumulated snow is included in a map, it leads to two main problems. First, during long-term driving, discrepancies between the actual and mapped environments, caused by melting snow, can significantly degrade localization performance. Second, the inclusion of large amounts of accumulated snow in the map can cause registration errors between sessions, thereby hindering accurate map updates. To address these issues, we propose a mapping strategy specifically designed for snow-covered environments. The proposed method first detects and removes accumulated snow using a deep learning-based approach. The resulting snow-free data are then used for map updating, and the ground information occluded by snow is subsequently restored. The effectiveness of the proposed method is validated with data collected in real-world snow-covered environments. Experimental results demonstrate that the proposed method achieves 78.6% IoU for snow detection and reduces map alignment errors by 12.5% (RMSE) and 15.6% (Chamfer Distance) on average, contributing to maintaining map quality and enabling long-term autonomous driving in snow-covered environments. Full article

Quantitative precipitation estimation (QPE) is one of the primary applications of weather radar. Over the last several decades, dual-polarization radars have significantly improved QPE accuracy by providing additional observational variables that offer more microphysical information about precipitation particles. In this work, we review QPE methods for dual-polarization radars and summarize their advantages and disadvantages from both theoretical and practical perspectives. The development paths and current status of operational QPE systems in the United States, China, and France are examined. We demonstrate how dual-polarization radars have improved QPE accuracy in these systems not only directly through the application of polarimetric QPE methods, but also indirectly through the more accurate identification of non-meteorological echoes, the mitigation of the partial blockage effect, and the detection of melting layers. The challenges are discussed for dual-polarization radar QPE, including the quality of polarimetric variables, QPE quality in complex terrain, estimation of surface precipitation with observations within or above the melting layer, and polarimetric QPE methods for snow. Full article

A shallow, seasonal snowpack is rarely homogeneous in depth, layer characteristics, or surface structure throughout an entire winter. Aerodynamic roughness length ($z_0$) is typically considered a static parameter within hydrologic and atmospheric models. Here, we present observations showing $z_0$ as a dynamic variable that is a function of snow depth ($d_s$). This has a significant impact on sublimation modeling, especially for shallow snowpacks. Terrestrial LiDAR data were collected at nine different study sites in northwest Colorado from the 2019 to 2020 winter season to measure the spatial and temporal variability of the snowpack surface. These data were used to estimate the geometric $z_0$ from 91 site visits. Values of $z_0$ decrease during initial snow accumulation, as the snow conforms to the underlying terrain. Once the snowpack is sufficiently deep, which depends on the height of the ground surface roughness features, the surface becomes more uniform. As melt begins, $z_0$ increases, when the snow surface becomes more irregular. The correlation value of $z_0$ was altered by human disturbance at several of the sites. The $z_0$ versus $d_s$ correlation was almost constant, regardless of the initial roughness conditions that only affected the initial $z_0$. Full article

Runoff simulation and forecasting is of great significance for flood control, disaster mitigation, and water resource management. Alpine regions are characterized by complex terrain, diverse precipitation patterns, and strong snow-and-ice melt influences, making accurate runoff simulation particularly challenging yet crucial. To enhance predictive capability and model applicability, this study takes the Upper Jinsha River as a case study and comparatively evaluates the performance of a physics-based hydrological model BTOP and the data-driven deep learning models LSTM and BiLSTM in runoff simulation and short-term forecasting. The results indicate that for daily-scale runoff simulation, the LSTM and BiLSTM models demonstrated superior simulation capabilities, achieving Nash–Sutcliffe efficiency coefficients (NSE) of 0.82/0.81 (Zhimenda Station) and 0.87/0.86 (Gangtuo Station) during the test period. These values are significantly better than those of the BTOP model, which achieved a validation NSE of 0.57 at Zhimenda and 0.62 at Gangtuo. However, the hydrology-based structure of the BTOP model endowed it with greater stability in water balance and long-term simulation. In short-term forecasting (1–7 d), LSTM and BiLSTM performed comparably, with the bidirectional architecture of BiLSTM offering no significant advantage. When it came to flood events, the data-driven models excelled at capturing peak timing and hydrograph shape, whereas the physical BTOP model demonstrated superior stability in flood peak magnitude. However, forecasts from the data-driven models also lacked hydrological consistency between upstream and downstream stations. In conclusion, the present study confirms that deep learning models achieve superior accuracy in runoff simulation compared to the physics-based BTOP model and effectively capture key flood characteristics, establishing their value as a powerful tool for hydrological applications in alpine regions. Full article

Deicing salt effectively melts ice and snow to maintain traffic flow in seasonal freezing zones, but its erosion effect compromises the water stability and structural integrity of asphalt pavements. To comprehensively explore the impacts of salt erosion on the interfacial behaviors of rubber asphalt–aggregate systems, this study developed a multiscale characterization method integrating a macroscopic mechanical test, microscopic tests, and molecular dynamics (MD) simulations. Firstly, laboratory-controlled salt–freeze–thaw cycles were employed to simulate field conditions, followed by quantitative evaluation of interfacial bonding properties through pull-out tests. Subsequently, the atomic force microscopy (AFM) and Fourier transform infrared spectrometer (FTIR) tests were conducted to characterize the microscopic morphology evolution and chemical functional group transformations, respectively. Moreover, by combining the diffusion coefficients of water molecules, salt solution ions, and asphalt components, the mechanism of interfacial salt erosion was elucidated. The results demonstrate that increasing NaCl concentration and freeze–thaw cycles progressively reduces interfacial pull-out strength and fracture energy, with NaCl-induced damage becoming limited after twelve salt–freeze–thaw cycles. In detail, with exposure to 15 freeze–thaw cycles in 6% NaCl solution, the pull-out strength and fracture energy of the rubber asphalt–limestone aggregate decrease by 50.47% and 51.57%, respectively. At this stage, rubber asphalt exhibits 65.42% and 52.34% increases in carbonyl and sulfoxide indexes, respectively, contrasted by 49.24% and 42.5% decreases in aromatic and aliphatic indexes. Long-term exposure to salt–freeze–thaw conditions promotes phase homogenization, ultimately reducing surface roughness and causing rubber asphalt to resemble matrix asphalt morphologically. At the rubber asphalt–NaCl solution–aggregate interface, the diffusion of Na⁺ is faster than that of Cl⁻. Meanwhile, compared with other asphalt components, saturates exhibit notably enhanced mobility under salt erosion conditions. The synergistic effects of accelerated aging, salt crystallization pressure, and enhanced ionic diffusion jointly induce the deterioration of interfacial bonding, which accounts for the decrease in macroscopic pull-out strength. This multiscale investigation advances understanding of salt-induced deterioration while providing practical insights for developing durable asphalt mixtures in cold regions. Full article

Winter road safety is threatened by black ice, while traditional de-icing methods, such as chemical spreading and electrically heated pavement systems, raise concerns about environmental impact and economic costs. This study proposed a hydronic heated pavement system utilizing geothermal energy (HHPS-G)-integrated concrete pavement that ensures environmental sustainability and structural stability. The design utilizes hollow steel pipes as both reinforcement and heat exchange conduits, thereby eliminating the need for separate high-density polyethylene (HDPE) pipes. To enhance upward heat transfer, bottom-ash concrete was introduced as an alternative to conventional insulation, providing thermal insulation and structural strength. A validated numerical model was developed to compare the de-icing and snow-melting performance of different pipe types. The results show that hollow steel pipes reduced the time to reach 0 °C on the concrete pavement surface by 30.86% and improved heat flux by 10.19% compared to HDPE. The depth of pipe installation significantly influenced performance: positioning the pipes near the surface achieved the fastest heating (up to 70.11% faster), while mid-depth placement, recommended for structural integrity, still provided substantial thermal benefits. Variations in insulation thermal conductivity below 1 W/m·K had little effect, whereas replacing the base layer with bottom-ash concrete provided both insulation and strength without the need for separate insulation layers. Full article

Conventional asphalt pavement snow and ice removal methods suffer from issues such as time-consuming operations, high costs, and pollution from chemical de-icing agents. Commonly used thermally conductive asphalt concrete (TCAC) faces problems including limited filler diversity, high filler content, and elevated costs. To address these challenges, this study developed a thermally conductive asphalt concrete incorporating carbon fiber–silicon carbide composite fillers to provide a low-cost, energy-saving winter pavement snow melting solution and enhance eco-friendly de-icing performance. Finite element simulation software was employed to model its snow and ice melting performance, investigating the factors influencing this capability. Thermal conductivity was measured using the transient plane source (TPS) technique. The results show that with 0.3% carbon fiber, thermal conductivity reaches 1.43 W/(m·°C), 72.3% higher than ordinary asphalt concrete. Finite element simulations in finite element simulation software were used to model snow and ice melting, and strong agreement with field test data (correlation coefficients > 0.9) confirmed model reliability. Then, the finite element simulation software was used to study the effects of wind speed, temperature, laying power, and spacing on the snow and ice melting of TCAC. The simulation results show that the heating rate increases with TCAC thermal conductivity. Raising the power of the embedded carbon fiber heating cord reduces de-icing time but shows a threshold effect. In this study, asphalt pavement with high thermal conductivity was prepared using a low content of thermal conductive filler, providing a theoretical basis for sustainable pavement design, reducing energy use and environmental damage. TCAC technology promotes greener winter road maintenance, offering a low-impact alternative to chemical de-icing, and supports long-term infrastructure sustainability. Full article

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

The glaciers of the Nyainqêntanglha Mountains serve not only as sensitive indicators of climate change, but also as important water sources for downstream rivers. In this study, we quantitatively analyzed the glacier mass balance of the entirety of the Nyainqêntanglha Mountains using TerraSAR-X/TanDEM-X and SRTM DEM data and compared the mass balance between glaciers in the western and eastern parts of the range, revealing the spatial heterogeneity in glacier mass loss. Finally, data from nine meteorological stations in the region were used to investigate regional climate changes and their impacts on glacier variation. The results show that from 2000 to 2013, the average annual glacier surface elevation in the Nyainqêntanglha Mountains decreased by 0.48 ± 0.02 m, with a mass balance of −0.55 ± 0.04 m water equivalent per year. The majority of glacier mass loss occurred in areas with slopes between 40° and 70°. The mass loss of clean glaciers in the eastern region was higher than that in the western region, whereas at high elevations, the mass loss of debris-covered glaciers was more severe in the western region than in the east. Overall, the debris cover on the glaciers has not yet reached the critical thickness required to effectively mitigate melting, and mass input in the accumulation zones is uneven, scattered, and limited, resulting in weak replenishment capacity. Against the backdrop of continued warming, regional precipitation is insufficient to provide the necessary accumulation, making glaciers more sensitive to rising temperatures. This study not only reveals pronounced spatial differences in glacier mass loss and their climatic drivers but also provides new scientific evidence for understanding water resource security, hydrological responses and potential snow avalanche hazards on the Tibetan Plateau, offering important implications for regional water management and future climate adaptation. Full article

This study examines ground surface and air temperatures and their implications for periglacial activity in the Țarcu Massif, Southern Carpathians, where data on current dynamics and climate responses remain scarce despite widespread periglacial landforms. To address this, we deployed seven temperature loggers between 2018 and 2024 across a range of periglacial landforms, including non-sorted patterned ground, a periglacial hummock, protalus rampart, block stream, periglacial tor, ploughing boulder, and nival niche. We analyzed key thermal indicators such as freeze–thaw cycles, freezing and thawing degree days, frost weathering intervals, frost days, and winter equilibrium temperatures—in relation to long-term air temperature records (1961–2023), snow cover dynamics, and local topographic and substrate conditions. Results reveal a marked warming trend at the Țarcu meteorological station, particularly after 1995, along with a shift in net thermal balance beginning in the late 1990s. Since then, climatic conditions at this site have no longer been favorable for the persistence of sporadic permafrost. Ground thermal conditions varied spatially, with coarse debris sites and rock wall maintaining the lowest MAGST values—typically with 1 to 2.5 °C cooler than fine-grained sediments—and the highest potential for frost-related weathering. Despite low and variable freeze–thaw cycle frequency, the high number of frost days (around 200 per year) and sustained frost weathering potential—exceeding 50 days annually at key sites—indicate that periglacial conditions remain active for nearly half the year around 2000 m in the Southern Carpathians. Snow cover dynamics proved to be a major control on ground thermal behavior, with earlier melting and delayed onset shortening its duration but amplifying early winter cooling. These findings indicate that the Țarcu Massif is a transitional periglacial environment, where active and relict features coexist under growing climatic pressure. The ongoing decline in frost-driven processes highlights the vulnerability of mid-latitude mountain periglacial systems to climate warming and underscores the need for continued monitoring to better understand future landscape evolution in the Southern Carpathians. Full article

Streamflow generated from snowmelt is important, and changing, in snow dominated regions of the world. We used a recently developed technique to estimate the start and end of snowmelt streamflow for 39 gauging stations across Colorado and determined the 40-year trends from 1981 to 2020. Most watersheds showed a trend towards an earlier start (34 watersheds) or end (29 watersheds) of snowmelt streamflow, but the mean of the start and end dates showed mixed trends (earlier in 12 watersheds and later in 20). We determined the correlation between these streamflow snowmelt trends and terrain parameters plus trends in canopy cover, winter precipitation, peak snow water equivalent, and melt-period temperature. There were some significant correlations, primarily for total annual streamflow and the timing and volume of the end of snowmelt streamflow contribution to winter precipitation (decreasing), minimum temperature (warming), and slope (negatively). Higher elevation watersheds tend to be steeper, less snow has been observed at higher elevations, and the snowpack is melting sooner. Snowmelt streamflow trends are partially explained by climate trends and watershed characteristics. Full article

Over the past 70 years, Central Asia has emerged as a globally recognized water security hotspot due to its unique geographic location and uneven distribution of water resources. In arid and semi-arid regions, understanding runoff dynamics under climate change is essential for ensuring regional water security. This study addresses the data-sparse Central Asian region by applying the ISIMIP3b multi-scenario analysis framework, selecting three representative global hydrological models. Using model intercomparison, trend analysis, and geographically weighted regression, we assess the spatiotemporal evolution of runoff from 1950 to 2080 and investigate the spatial heterogeneity of runoff responses to precipitation and temperature. The results show that under the historical scenario, all models consistently identify similar spatial pattern of runoff, with higher values in southeastern mountainous regions and lower values in western and central regions. However, substantial differences exist in runoff magnitude, with regional annual means of 10, 26, and 68 mm across the three models, respectively. The spatial disparity of runoff distribution is projected to increase under higher SSP scenarios. During the historical period, most of Central Asia experienced a slight decreasing trend in runoff, but the overall trends were −0.022, 0.1, and 0.065 mm/year, respectively. In contrast, future projections indicate a transition to increasing trends, particularly in eastern regions, where trend magnitudes and statistical significance are notably greater than in the west. Meanwhile, the spatial extent of significant trends expands under high-emission scenarios. Precipitation exerts a positive influence on runoff in over 80% of the region, while temperature impacts exhibit strong spatial variability. In the WaterGAP2-2e and MIROC-INTEG-LAND models, temperature has a positive effect on runoff in glaciated plateau regions, likely due to enhanced snow and glacier melt under warming conditions. This study presents a multi-model framework for characterizing climate–runoff interactions in data-scarce and environmentally sensitive regions, offering insights for water resource management in Central Asia. Full article

Reliable monitoring of snow cover in mountainous regions remains a challenge due to frequent cloud cover and the revisit limitations of optical satellites. This study compares satellite snow-cover records with >99,000 ground-based time-lapse camera observations across northern Spain (2003–2025). Cloud cover caused major data loss, with up to 57% of satellite images affected. Effective revisit intervals (the average time between usable images) diverge substantially from nominal values: 2.3 days for MODIS, 6.9 days for Sentinel-2, and over 21 days for Landsat. A hierarchical multisensor approach with 5-day gap-filling reduced this to just 1.3 days. On dates when cameras confirmed snow, satellites underestimated snow presence by 61.6% (Sentinel-2), 71.5% (Landsat), and 79.7% (MODIS), though gap-filling approaches reduced underestimation to 49.4%—deficits largely attributable to cloud-obscured scenes. When both satellite and camera provided cloud-free observations for the same date and location, classification agreement exceeded 85%. Despite this, satellites consistently failed to detect short-lived snow events and introduced temporal biases. On average, Snow Onset Dates were detected 13–52 days later, and Snow Melt-Out Dates differed by up to 40 days compared to camera-derived records. These results have implications for snow-cover monitoring using satellite images and highlight the need for integrating ground-based observations to compensate for satellite limitations and improve snow cover seasonality assessments in complex terrains. Full article

Snow and ice accumulation on transportation infrastructure presents significant safety and maintenance challenges in cold regions, while conventional removal methods are both energy-intensive and environmentally detrimental. This study proposes a passive Heat Pipe–Coupled Geothermal Snow Melting System (HP-GSMS) that harnesses shallow geothermal energy to maintain snow-free surfaces without external energy input. Using Fluent-based CFD simulations, the system’s thermal performance was evaluated under various working fluids (ammonia, carbon dioxide, water) and pipe materials (stainless steel, aluminum). A one-dimensional thermal resistance model validated the CFD results under ammonia–stainless steel conditions, predicting a heat flux of 358.6 W/m² compared to 361.0 W/m² from the simulation, with a deviation of only 0.66%, confirming model accuracy. Ammonia demonstrated superior phase-change efficiency, with the aluminum–ammonia configuration yielding the highest heat flux (up to 677 W/m²), surpassing typical snow-melting thresholds. Aluminum pipes enhanced radial heat conduction without compromising phase stability, while water exhibited poor phase-change performance and CO₂ showed moderate but stable behavior. Additionally, a dynamic three-node RC thermal network was employed to assess transient performance under realistic diurnal temperature variations, revealing surface heat fluxes ranging from 230 to 460 W/m², with a daily average of approximately 340 W/m². These findings demonstrate the HP-GSMS’s practical viability in cold climates and underscore the importance of selecting low-boiling-point fluids and high-conductivity materials for scalable, energy-efficient, and low-carbon snow-melting applications in urban infrastructure. Full article

Snow serves as a crucial water source for vegetation growth on the Mongolian Plateau, and its temporal and spatial variations exert profound influences on terrestrial vegetation phenology. In recent years, global climate change has led to significant changes in snow and vegetation start of growing season (SOS). Therefore, it is necessary to study the mechanism of snow cover on vegetation growth and changes on the Mongolian Plateau. The study found that the spatial snow cover fraction (SCF) of the Mongolian Plateau ranged from 50% to 60%, and the snow melt date (SMD) ranged from day of the year (DOY) 88 to 220, mainly concentrated on the northwest Mongolian Plateau mountainous areas. Using different SOS methods to calculate the vegetation SOS distribution map. Vegetation SOS occurs earlier in the eastern part compared to the western part of the Mongolian Plateau. In this study, we assessed spatiotemporal distribution characteristics of snow on the Mongolian Plateau over the period from 2001 to 2023. The results showed that the SOS of the Mongolian Plateau was mainly concentrated on DOY 71-186. The Cox survival analysis model system established SCF and SMD on vegetation SOS. The SCF standard coefficient is 0.06, and the SMD standard coefficient is 0.02. The SOS_NDVI coefficient is −0.15, and the SOS_NDGI coefficient is −0.096. The results showed that the vegetation SOS process exhibited differential response characteristics to snow driving factors. These research results also highlight the important role of snow in vegetation phenology and emphasize the importance of incorporating the unique effects of vegetation SOS on the Mongolian Plateau. Full article