Heavy Snowfall Research Articles

Elevation dependency of snowfall changes under climate change over the tibetan plateau: Evidence from CMIP6 GCMs

Snowfall plays a crucial role in the mountainous cryosphere cycle and is significantly influenced by climate change. This study utilizes the global climate models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) with multivariate bias correction (MBC) to explore potential future variations in snowfall and its elevation dependency across the Tibetan Plateau (TP). Findings indicate a consistent decline in annual snowfall across the majority of the TP by the end of the century, except for certain high-elevation regions in the northwest. The decreasing trend is projected to intensify with strengthen Shared Socioeconomic Pathway (SSP) scenarios and exhibits elevation dependency below 5000 m. Specifically, under the SSP5–8.5 scenario, snowfall over the TP is expected to decrease by 39.74 % in the far future (2071–2100), with the elevation zone below 2000 m experiencing the most intense decline of approximately 62 %. This trend is largely attributed to the significant warming, which reduces the snow fraction as more precipitation falls as rain rather than snow. This shift is evidenced by the identification of turning points in snow fraction in the mid-2040s to 2050s, coinciding with rapid temperature increases. Furthermore, substantial decreases in future (heavy) snowfall days contribute to the overall reduction in snowfall. However, complex interplay between increased precipitation and temperature effects results in a slight increase in snowfall over high elevation areas in the northern edge. Uncertainty analysis indicates model uncertainty as the dominant source in snowfall projections, accounting for over 50 % of total variance. The projected declines in snowfall and snow fraction, as well as shortened snowfall days could considerably impact the cryosphere, hydrological and ecological systems of the TP.

Sec-CLOCs: Multimodal Back-End Fusion-Based Object Detection Algorithm in Snowy Scenes

LiDAR and cameras, often regarded as the “eyes” of intelligent driving vehicles, are vulnerable to adverse weather conditions like haze, rain, and snow, compromising driving safety. In order to solve this problem and enhance the environmental sensing capability under severe weather conditions, this paper proposes a multimodal back-end fusion object detection method, Sec-CLOCs, which is specifically optimized for vehicle detection under heavy snow. This method achieves object detection by integrating an improved YOLOv8s 2D detector with a SECOND 3D detector. First, the quality of image data is enhanced through the Two-stage Knowledge Learning and Multi-contrastive Regularization (TKLMR) image processing algorithm. Additionally, the DyHead detection head and Wise-IOU loss function are introduced to optimize YOLOv8s and improve 2D detection performance.The LIDROR algorithm preprocesses point cloud data for the SECOND detector, yielding 3D object detection results. The CLOCs back-end fusion algorithm is then employed to merge the 2D and 3D detection outcomes, thereby enhancing overall object detection capabilities. The experimental results show that the Sec-CLOCs algorithm achieves a vehicle detection accuracy of 82.34% in moderate mode (30–100 m) and 81.76% in hard mode (more than 100 m) under heavy snowfall, which demonstrates the algorithm’s high detection performance and robustness.

Atmospheric concentrations of 7Be and 210Pb in weekly aerosols at Hirosaki, a heavy snowfall area in Japan.

To understand the regional properties of atmospheric 7Be and 210Pb concentrations at Hirosaki, aerosol samples were collected weekly for the investigation. Total suspended particles (TSP) concentration ranged from 0.14 to 1.19mgm-3 with a mean value of 0.29±0.08mgm-3. The activity concentrations during the observation period have shown a similar trend between 7Be and 210Pb, comparable to reported studies. The activity concentrations of 7Be and 210Pb ranged from 0.46 to 4.94 mBq m-3 with an average of 2.22 mBq m-3 and from 0.26 to 1.29 mBq m-3 with an average of 0.64 mBq m-3, respectively. The relation between precipitation and radionuclide concentrations indicated that precipitation is an essential factor in the radionuclide removal process. The rainfall/snowfall had a significant impact on the deposition, and the process was greatly affected by the changes in the features of raindrops and snowflakes, such as intensity, shape, surface area, and falling speed.

Cascading effects driven by population recovery of sika deer on habitat use of sympatric mammals under heavy snow conditions

AbstractUnlike the top‐down cascading effects driven by apex predators, the bottom‐up effects on biological interactions originating from herbivory by large mammals, especially within mammalian assemblages, have received less attention. This study aimed to identify the multifaceted impacts driven by sika deer (Cervus nippon) during midwinters, when their herbivory impacts were expected to be strong due to deficient dietary resources. We focused on interference and exploitative competition caused by deer on the habitat use of four sympatric mammal species, namely two herbivores (Japanese serow, Capricornis crispus, and Japanese hare, Lepus brachyurus angustidens) and two mesocarnivores (red fox, Vulpes vulpes, and Japanese marten, Martes melampus melampus). To estimate the wintering behaviors of these mammals and deer herbivory pressure, we performed snow tracking and counted the fresh feeding marks of deer during midwinter in 2022 and 2023. We conducted surveys on 201‐km transects with different stages of deer population recovery (i.e., different deer population densities) in heavy snowfall regions of Japan. We then systematically quantified the deer‐driven effects on sympatric mammals using piecewise structural equation modeling (SEM). We recorded 1700 tracks of target mammals and 1327 trees with deer feeding marks. Based on these records, we constructed an SEM with acceptable performance (Fisher's C = 27.1, P = 0.30). The SEM suggested some possibilities that exploitative competition mediated by deer herbivory constrained the habitat use of mammals with dietary habits similar to that of deer, i.e., serow (effect size, −0.13) and hares (−0.14). In addition, through constricting the habitat use of hares, the deer‐driven cascading effects reduced the occurrence of their key predators, i.e., foxes (−0.12) and martens (−0.10). Thus, our observations provide novel evidence that food webs are regulated by herbivore‐driven bottom‐up cascading effects in cases where plant primary productivity is limited by heavy snowfall.

Advances in Cold-Climate-Responsive Building Envelope Design: A Comprehensive Review

Extreme low temperatures, heavy snowfall, ice accumulation, limited daylight, and increased energy consumption in cold climates present significant challenges but also offer opportunities for improving building efficiency. Advanced materials and technologies in climate-responsive envelopes can enhance sustainability, reduce carbon footprints and operational costs, and improve thermal comfort under these environmental conditions. This literature review combines theoretical aspects of building performance in cold climates with a summary of current and critical applications in building envelope design, identifying research gaps and proposing future research directions. It has been shown that various BIPV systems require further climate-based studies to optimize solar energy yield. For example, integrating PV layers and PCM within DSFs can reduce cooling loads, but more research is needed on PCM transition temperatures and ventilation strategies in cold climates. A notable research gap exists in building-integrated vegetative systems, particularly regarding soil thickness, irrigation, hygrothermal performance, and snow accumulation. Despite excellent winter performance in buildings incorporating CLT components, they face increased cooling energy consumption and potential overheating in summer. Additionally, the high initial moisture content in CLT raises the risk of mold growth, especially when covered with vapor-tight layers. The design examples in this paper emphasize the need for further investigation to achieve sustainable, low-carbon, energy-efficient envelope designs for cold climates.

Classification of risk levels for snow damage estimation considering socioeconomic factors in South Korea

In South Korea, the snowy season spans from October to April, and the annual average snowfall varies significantly depending on specific regions, latitudes, and elevations, ranging from 0 to 260 cm. The average annual snowfall in South Korea is 25.1 cm. Despite of the relatively shallow snowfall depth, over the past decade, South Korea has experienced approximately 120 million dollars in damages attributed to snow-related incidents. In this study, the DPSIR (Driver-Pressure-State-Impact-Response) framework was employed to consider the meteorological and socioeconomic factors to calculate the snow damage vulnerability. A total of 17 indicators were taken into account to comprehend meteorological conditions, socioeconomic factors, and historical damage records from 1994 to 2020. However, due to the limited availability of meteorological observatories and changes in greenhouse design standards, accurately estimating the snow damage amount poses challenges. Therefore, based on the vulnerability, the risk levels were classified into four categories and estimated snow damage generated by the categorized models was compared with those of the model constructed using the entire dataset. The categorized models offer improved estimation results, as the meteorological and socioeconomic characteristics within each category differ and should be addressed separately in modeling. Among the categorized models, the Green zone exhibited the best results, primarily because it did not include outlier snow damage incidents. The developed model in this study could be utilized to mitigate the impact of heavy snowfall and prioritize snow removal regions.

Impact of the combined assimilation of GPM/IMGER precipitation and Himawari-8/AHI water vapor radiance on snowfall forecasts using WRF model and 4Dvar system

Impact of the combined assimilation of GPM/IMGER precipitation and Himawari-8/AHI water vapor radiance on snowfall forecasts using WRF model and 4Dvar system

An extreme precipitation event over Dronning Maud Land, East Antarctica - A case study of an atmospheric river event using the Polar WRF Model

Extreme precipitation events (EPEs) are crucial in Antarctica, impacting the Antarctic ice sheet's surface mass balance and stability. Comprehensive case studies are essential for better understanding these events and the underlying processes driving them. Here, we investigate an extreme snowfall event in Dronning Maud Land (DML), East Antarctica on November 8 and 9, 2015. This event contributed approximately 22 % of the annual accumulation in less than two days and exhibited high spatial variability in precipitation distribution. We employed a high-resolution atmospheric model specifically optimized for the polar regions (Polar WRF) and ERA5 reanalysis data to analyze the event in detail. Our findings highlight the importance of a blocking high-pressure ridge of record strength that effectively blocked and diverted a strong extratropical cyclone into DML, ultimately leading to the heavy snowfall event. The sudden deepening of the cyclone was initiated by a ‘jet streak’ in the upper atmosphere that steered the system southeastwards towards the Antarctic coast. Notably, we observed an anomalously high poleward moisture transport in the form of a strong atmospheric river on November 7, 2015. This atmospheric river originated in the South Atlantic Ocean and tracked poleward from the 30°S-40°S latitude band. Vertical cross-sections of the model outputs indicate that most of the precipitation was concentrated in regions with steep orography along the path of the atmospheric river. This interaction between the atmospheric river and the steep terrain led to the uplift of maritime air, resulting in heavy snowfall. This study highlights the significance of extreme upper and lower atmospheric conditions in driving intense moisture transport towards coastal DML. The interaction between the atmospheric river and the steep orography contributed to heavy snowfall, underscoring the importance of considering orographic influences in understanding EPEs in Antarctica.

Future Change in the Contribution of Riming and Depositional Growth to the Surface Solid Precipitation in Hokkaido, Japan

Abstract This study examined future changes in the microphysical properties of surface solid precipitation over Hokkaido, Japan. A process-tracking model that predicts the mass of the hydrometeors generated by each cloud microphysical process was implemented in a meteorological model. This implementation aimed to analyze the mass fraction of hydrometeors resulting from depositional growth and the riming process to the total mass of surface solid precipitation. Results from pseudo–global warming experiments suggest two potential future changes in the characteristics of surface solid precipitation over Hokkaido. First, the rimed particles are expected to increase and be dominant over the west and northwest coast of Hokkaido, where heavy snowfall occurs primarily due to the lake effect. Second, the mass fraction from depositional growth under relatively higher temperatures is expected to increase. This increase is anticipated to be dominant over the eastern part and mountainous area of Hokkaido. Additionally, the fraction of liquid precipitation to total precipitation is expected to increase in the future. These results suggest that the microphysical properties of solid precipitation in Hokkaido are expected to be similar to those observed in the current climate over Hokuriku, the central part of Japan even in warmer climate conditions. Significance Statement This study examines potential future changes in the growth processes contributing to surface precipitation particles in Hokkaido, Japan. The surface solid precipitation particles in the western and eastern regions of Hokkaido are mainly generated through depositional growth that occurs within the temperature ranges −36° to −20°C and −20° to −10°C, respectively. A future shift is anticipated, with riming becoming the primary process. This shift suggests that snowfall particles will be heavier than those in the current climate, which would increase the snow-removal workload. The change in precipitation characteristics could influence adaptation and mitigation strategies for climate change in cold regions.

Operation, Expansion, and Improvement of the Snow Load Alert System “YukioroSignal”

The “YukioroSignal” system, which provides snow load alerts, was developed to inform decision-making regarding snow removal from house roofs. It was launched in Niigata Prefecture in 2018 and expanded to cover all special heavy snowfall areas in Japan, including the Hokkaido, Tohoku, and Hokuriku regions in 2024. The system uses the SNOWPACK model to estimate high-accuracy snow weight from real-time snow depth data published online at observation points. At locations where snow depth gauges are not installed, such as in mountainous areas, snow weight is estimated using inverse distance-weighted interpolation, but accuracy is reduced. To overcome this problem, this study attempted to integrate this information with the snow water equivalent distribution calculated using the simple-layer snow distribution model. To validate this improvement, manual observations of snow weight were performed at 98 sites and compared with simulation results. The accuracy of snow weight estimation at distances far away from snow depth stations was improved. The six-year operation of YukioroSignal showed the additional required information that is vulnerable to damage even with less snowfall, such as vacant houses, and caution of changes in hazard levels by an increase in snowburst in a short period.

Investigating the performance of PBL parametrizations in WRF model for front enhanced SES simulation in istanbul megacity

Sea-effect snow (SES) is a meteorological phenomenon resulting from cold air moving over warmer waters. Accurate prediction of SES is vital for emergency management, transportation, and water resource planning. A thundersnow event in Istanbul from 17–19 February 2015 caused significant disruptions, with traffic and flights affected, highways temporarily closed, and trees falling due to heavy snowfall. This study investigates the influence of different parameterization schemes in the Weather Research and Forecasting (WRF) model on SES simulations. Six distinct PBL parameterization schemes were used in a series of WRF simulations. In addition, the following factors pivotal to SES event have also been investigated: 1000–500 hPa thickness, total and latent heat fluxes, radar and satellite analyses, temperature gradients, wind shear, inversion levels, and atmospheric stability indices. Additionally, the formation of SES during the cold front transition further contributed to these elements in the Black Sea region. The simulations displayed notably high total heat flux and latent heat flux values, particularly following the passage of the cold front. Furthermore, the northeast-southwest oriented SES cloud, distinguished by its banded structure, was successfully validated using radar and satellite imagery. However, it's worth noting that the model positioned it farther west than its actual location. This study highlights the challenges in precise prediction and analysis of such convective activities. In this thundersnow event, the local closure schemes, particularly MYNN in first place and second MYJ, demonstrated superior performance compared to non-local schemes within the parameterization options.

Stakeholder perceptions, challenges, and sustainable solutions for achieving safely managed sanitation in Kazakhstan: a case study from a cold region

ABSTRACT In this paper, we present survey results from 30 sanitation stakeholders across the public and private sectors, professional associations, non-governmental organisations (NGOs), and households in the two coldest cities of Kazakhstan. The objectives were to identify the perceived sanitation challenges, document innovative, and traditional solutions employed by the stakeholders to manage sanitation challenges during the cold season, and to propose initiatives and policy interventions to alleviate pressing sanitation issues. Commonly encountered problems included improper installation of septic tanks (e.g. above the frost line) by non-professionals and the difficulty of households to access latrines during heavy snowfall events. Ninety-three percent of respondents expressed that foul odours and sewage overflows were their primary concerns, with only 7% reporting health impacts as a primary concern. There were significant differences in the viewpoints of the respondents regarding the extent of sanitation infrastructure maintenance or upgrades needed in the two cities, suggesting regional differences across the country rather than a uniform national situation. In the context of Kazakhstan, local NGOs were identified as playing a key role in engaging with households to co-devise affordable and sustainable sanitation solutions that are suitable for cold regions, which can then be communicated to other sanitation stakeholders, including the public sector.

Exploring botanical varieties in alpine landscape of Himalayas: A study of vegetation and species composition in Madhmaheshwar Valley, Western Himalaya, India

Alpine meadows emerged as the hallmark vegetation type, embodying the essence of botanical richness. In this research endeavor, the focus was on exploring the intricate tapestry of alpine flora nestled within the Madhmaheshwar Valley, spanning elevations ranging from 3200 to 4950 meters above sea level. This comprehensive study yielded a trove of botanical insights, documenting an impressive assemblage of 462 distinct plant species. These species were carefully cataloged across 237 genera, encompassing a rich diversity represented by 61 distinct families. In the study area, the family Asteraceae was the dominant family with 58 species and 30 genera followed by Rosaceae with 33 species and 16 genera, and Ranunculaceae with 27 species across 15 genera. Among the genera Carex L., with 10 species, was the dominant, followed by Saxifraga Tourn. ex L., Gentiana Tourn. ex L., Pedicularis L., and Primula L. followed closely behind with nine species each. Additionally, Rhodiola L., Bistorta (L.) Scop., and Epilobium Dill. ex L. displayed their vitality with seven species each. The study used meticulous research methodology to categorize forest types based on floral diversity and altitude distribution. Rigorous verification processes ensured the reliability of findings, with specimens identified using up-to-date references and cross-referenced with authentic herbarium samples. Fieldwork occurred between early May and late October, aligning with seasonal accessibility due to heavy snowfall the rest of the year. Alpine meadows and scrubs dominated the study area, highlighting their resilience. Alpine plants showcased various adaptive morphologies, such as cushion-forming and mat-forming structures, with thick cuticles defending against desiccation. Despite harsh conditions, these plants displayed modified structures enabling growth and blossoming. Many were highly specialized, emphasizing unique adaptations to their habitats.

Temporal Differences in Winter Habitat Use by Japanese Hares (Lepus brachyurus) in a Heavy Snowfall Environment

Temporal Differences in Winter Habitat Use by Japanese Hares (Lepus brachyurus) in a Heavy Snowfall Environment

Snow simulation for the rangeland hydrology and erosion model

In the western US, most rangelands receive snowfall. Yet, a commonly used tool to assess rangeland’s vulnerability to erosion, the USDA’s Rangeland Hydrology and Erosion Model (RHEM) is run using long-term simulated climate inputs that assumes that all precipitation occurs as rainfall. This can be problematic for areas that receive heavy snowfall or substantial rain-on-snow events. In this research, we have developed an efficient snow module for RHEM, called RHEM-Snow, which partitions precipitation between rainfall and snowfall, simulates snowpack accumulation and ablation, and passes net water input (consisting of rainfall, snowmelt, or both) to RHEM. In some areas, the inclusion of the snow module can reduce annual overland flow runoff and erosion estimates by more than 20 % of the total annual overland flow runoff and erosion produced without the snow module (or by as much as 10–50 mm/year for overland flow runoff or >100 kg/ha-yr for erosion). The reclassification of precipitation events from rainfall in RHEM to snowfall in RHEM-Snow tends to reduce overland flow runoff and erosion, but this reduction can be partially counterbalanced by increases from snowmelt and rain-on-snow. However, hydrologic responses to rain-on-snow events can either be enhanced or muted depending on the characteristics of the storm and the snowpack, as sometimes the snowpack can absorb the precipitation inputs, and sometimes snowmelt enhances the precipitation inputs. Because of this mixed impact, the average difference in erosion caused by rain on snow events is relatively small compared to corresponding events where only the liquid phase is considered. Further study is needed of the complex erosion processes under snowpack and frozen soil/variable saturation conditions. Overall, RHEM-Snow provides more realistic timing and magnitude of overland flow runoff and erosion in cold environments, better satisfying the conditions for RHEM applications.

Image analysis and LSTM methods for forecasting surficial displacements of a landslide triggered by snowfall and rainfall

AbstractLandslide-prone areas, predominantly located in mountainous regions with abundant rainfall, present unique challenges when subject to significant snowfall at high altitudes. Understanding the role of snow accumulation and melting, alongside rainfall and other environmental variables like temperature and humidity, is crucial for assessing landslide stability. To pursue this aim, the present study focuses first on the quantification of snow accumulated on a slope through a simple parameter obtained with image processing. Then, this parameter is included in a slope displacement prediction analysis carried out with long short-term memory (LSTM) neural network. By employing image processing algorithms and filtering out noise from white-shown rocks, the methodology evaluates the percentage of snow cover in RGB images. Subsequent LSTM forecasts of landslide displacement utilize 28-day historical data on rainfall, snow, and slope movements. The presented procedure is applied to the case of a deep-seated landslide in Italy, a site that in winter 2020–2021 experienced heavy snowfall, leading to significant snow accumulation on the slope. These episodes motivated a study aimed at forecasting the superficial displacements of this landslide, considering the presence of snow both at that time and in the following days, along with humidity and temperature. This approach indirectly incorporates snow accumulation and potential melting phenomena into the model. Although the subsequent winters were characterized by reduced snowfall, including this information in the LSTM model for the period characterized by snow on the slope demonstrated a dependency of the predictions on this parameter, thus suggesting that snow is indeed a significant factor in accelerating landslide movements. In this context, detecting snow and incorporating it into the predictive model emerges as a significant aspect for considering the effects of winter snowfall. The method aims to propose an innovative strategy that can be applied in the future to the study of the landslide analyzed in this paper during upcoming winters characterized by significant snowfall, as well as to other case studies of landslides at high altitudes that lack precise snow precipitation recording instruments.

Reorganization of Snowfall beneath Cloud Top within the Comma Head Region of Two Extreme U.S. East Coast Winter Cyclones

Abstract This paper examines ice particle reorganization by three-dimensional horizontal kinematic flows within the comma head regions of two U.S. East Coast winter storms and the effect of reorganization on particle concentrations within snowbands in each storm. In these simplified experiments, the kinematic flows are from the initialization of the HRRR model. Ice particles falling through the comma head were started from either 9-, 8-, or 7-km altitude, spaced every 200 m, and were transported north or northwest, arriving within the north or northwest half of the primary snowband in each storm. The greatest particle concentration enhancement within each band was a factor of 2.32–3.84 for the 16–17 December 2020 storm and 1.76–2.32 for the 29–30 January 2022 storm. Trajectory analyses for particles originating at 4 km on the southeast side of the comma head beneath the dry slot showed that this region supplied particles to the south side of the band with particle enhancements of factor of 1.36–2.08 for the 16–17 December 2020 storm and 1.04–2.16 for the 29–30 January 2022 storm. Snowfall within the bands had two source regions: 1) on the north/northwestern side, from ice particles falling from the comma head, and 2) on the southeastern side, from particles forming at or below 4-km altitude and transported northwestward by low-level flow off the Atlantic. While the findings give information on the source of particles in the bands, they do not definitively determine the cause of precipitation banding since other factors, such as large-scale ascent and embedded convection, also contribute to snow growth. Significance Statement Wintertime storms along the east coast of North America can produce heavy snowfall, high winds, coastal flooding, and cold temperatures, resulting in major economic impacts within the northeast U.S. urban corridor. The heaviest snowfall typically occurs within snowbands, elongated narrow regions identifiable by high reflectivity on radar. This paper examines the potential sources of the ice particles contributing to the snowbands and how the flow fields throughout the storm can contribute to enhanced particle concentrations within the bands.

Technical note: Surface fields for global environmental modelling

Abstract. Climate change has resulted in more frequent occurrences of extreme events, such as flooding and heavy snowfall, which can have a significant impact on densely populated or industrialised areas. Numerical models are used to simulate and predict these extreme events, enabling informed decision-making and planning to minimise human casualties and to protect costly infrastructure. LISFLOOD is an integrated hydrological model underpinning the European Flood Awareness System and Global Flood Awareness System (EFAS and GloFAS, respectively), developed by the Copernicus Emergency Management Service (CEMS). The CEMS_SurfaceFields_2022 dataset is a new set of high-resolution surface fields at 1 and 3 arcmin resolution (approximately 2 and 6 km at the Equator, respectively) based on a wide variety of high-resolution and up-to-date data sources. The 1 arcmin fields cover Europe, while the surface fields at 3 arcmin cover the global land surface (excluding Antarctica). The dataset encompasses (i) catchment morphology and river networks, (ii) land use, (iii) vegetation cover type and properties, (iv) soil properties, (v) lake information, and (vi) water demand. This paper details the complete workflow used to generate the CEMS_SurfaceFields_2022 fields, including the data sources and methodology. Whilst created together with upgrades to the open source LISFLOOD code, the CEMS_SurfaceFields_2022 fields can be used independently for a wide range of applications, including as input to hydrological, Earth system, or environmental models or for carrying out general analyses across spatial scales, ranging from global and regional levels to local levels (especially useful for regions outside Europe), expected to improve the accuracy, detail and realism of applications.

Effect of Freeze-Thaw on CBR in Soils with Different Gradation and Mineralogy

Freeze-thaw cycles are prevalent climatic phenomena with substantial effects on soils, leading to alterations in soil strength, stiffness, and hydraulic properties due to disruptions in the soil structure. With the ongoing climate change, weather patterns have grown progressively erratic, resulting in more frequent occurrences of extreme weather events, including heavy snowfall, intense rainfall, and windstorms, even in regions characterized typically with mild climates across the globe. The climate change can potentially threat man-made infrastructure constructed within or upon local soils, regardless of their susceptibility to freezing in temperate climates. The principal objective of this study is to assess the influence of freeze-thaw cycles on the California Bearing Ratio (CBR %) across 12 distinct soils with variations in granulometry and mineralogy. The freeze-thaw cycles resulted in a notable decrease in CBR (%) within the range of 40% to 70%. A strong inverse correlation with D50 was observed regarding the decrease in CBR (%). Nevertheless, it was discerned that the decrease in CBR (%) subsequent to freeze-thaw cycles varied among soil samples sharing identical D50 and liquid limit characteristics. The aim of this study is to enhance our comprehension of how freeze-thaw cycles can impact the bearing capacity of these soils, thereby providing essential insights for predicting their behavior and potential influence on infrastructure in the context of climate change.

Green smart multifunctional wooden roofs enabled by single-step hydrophobic laser-induced graphene fabrication

Global warming, contributing to the worsening climate crisis, has led to more frequent heavy snowfall and rainfall. This causes corrosion, mold, and structural damage to roofs, which shortens the roofs' lifespan and potentially creates safety hazards. Addressing these challenges, we propose an approach to develop a multifunctional roof crucial for proactive adaptation to extreme weather. In this article, we present hydrophobic laser-induced graphene (LIG) onto wood in a single-step process using femtosecond-laser-direct-writing (FsLDW) technology in ambient air without additional chemical treatment. This hydrophobic LIG, showcasing a high electrical conductivity of 10.0 Ω·sq⁻1 and a high contact angle of 148.8°, seamlessly integrates onto roofs, creating cost-effective, fast, eco-friendly, and smart roofing solutions. The hydrophobic surface of these LIG electrodes incorporates a heating function of LIG showcasing their versatility in providing waterproofing, drying wood, and facilitating de-icing functions. This eco-friendly invention, reliant solely on laser patterning on wood, not only extends roof lifespan but also relieves concerns about electronic waste and recycling, promising the integration of green, smart, and sustainable roofing solutions.