Heavy Snowfall Research Articles

Effect of Social Distancing Caused by Heavy Snowfall on Seasonal Influenza Epidemic in a Local Region in Japan.

The effectiveness of social distancing in controlling influenza epidemics is debatable. We analyzed the local impact of large-scale social distancing caused by heavy snowfall on influenza epidemic. Surveillance data from the National Institute of Infectious Diseases on influenza diagnosed at registered Fukui Prefecture institutions were plotted in epidemic curves. We also compared the number of elementary and junior high school students suspended attendance due to influenza during the 2017/2018 season between the northern and southern Fukui Prefecture territories. Weekly incidence influenza rates and average rates before, during and after school closure caused by heavy snowfall in the northern territory were compared with those of the southern territory using Poisson generalized estimating equations to account for school variability. The northern territory epidemic curve, like the nationwide trend, rapidly declined during the heavy snowfall and formed a second peak 4 weeks later. The southern territory's curve was like the nationwide trend throughout. Weekly incidence rate ratios in schools for the northern versus southern territories varied from 0.23 during heavy snowfall to 2.32, 4 weeks after the snowfall. Cumulative incidence ratio (95% confidence interval) of the northern-to-southern territories was 0.92 (0.74-1.13; P = 0.42). School closure significantly reduced the incidence rate to 0.41 (95% confidence interval, 0.19-0.89; P = 0.02). Although relatively large-scale, simultaneous school closures and social distancing could temporarily suppress seasonal influenza epidemic, they could not impede the cumulative incidence. Large-scale social distancing may contribute to slowing infection spread during seasonal influenza epidemics.

Let It Snow: Designing Snowfall Experience in VR

We present Snow, a cross-modal interface that integrates cold and tactile stimuli in mid-air to create snowflakes and raindrops for VR experiences. Snow uses six Peltier packs and an ultrasound haptic display to create unique cold-tactile sensations for users to experience catching snowflakes and getting rained on their bare hands. Our approach considers humans' ability to identify tactile and cold stimuli without masking each other when projected onto the same location on their skin, making illusions of snowflakes and raindrops. We design both visual and haptic renderings to be tightly coupled to present snow melting and rain droplets for realistic visuo-tactile experiences. For multiple snowflakes and raindrops rendering, we propose an aggregated haptic scheme to simulate heavy snowfall and rainfall environments with many visual particles. The results show that the aggregated haptic rendering scheme demonstrates a more realistic experience than other schemes. We also confirm that our approach of providing cold-tactile cues enhances the user experiences in both conditions compared to other modality conditions.

Analyzing the impact of socio-environmental parameters on wheat and barley cultivation areas using the geographical detector model

The national economy of Iranians depends greatly on agriculture. Wheat and barley are two strategic crops in Iran, where their scarcity can adversely affect people's quality of life. Therefore, it is essential to analyze the climatic, humidity-related, and socioeconomic factors affecting the cultivation areas of these crops. This study analyzed the single and mutual effects of 17 parameters on wheat and barley cultivation areas in Iran within the 2007–2020 period. The geographical detector model (GDM) was used in RStudio to analyze the interactive effects of parameters. According to the results, topography had greater effects than other parameters on the cultivation areas of wheat. Spring precipitation had a larger impact with 26% of effects than winter precipitation (SC) with 24% of effects on the cultivation area of wheat. However, winter precipitation and temperature with 42% and 34% effects, respectively, had greater impacts than spring precipitation on the cultivation area of barley. NDVI and SC have the highest impact on the cultivated area of wheat and barley crops. Also, elevation is the most important parameter in wheat cultivation with 54% influence, and this product performs better in high altitudes, on the other hand, elevation is not a vital factor in barley. Also, the winter parameters have a greater effect on wheat than barley, and the average wheat harvest showed high performance in heavy snowfall lands. Also, the results showed that barley has a higher resistance to dry conditions. Moreover, socioeconomic parameters had more effects on barley than on wheat. In other words, livestock cover with 52% of effects had the greatest impact on the cultivation area of barley, whereas population with 36% of effects was ranked the third factor affecting the cultivation area of wheat. According to the interactive results of parameters, the interaction between elevation and NDVI was 88% in wheat. However, the interaction between population and livestock cover was 72% in barely. The optimal results of barley and wheat indicated that both crops had the largest cultivation areas at 22.9–24.9 °C.

Use possibility analysis for wind energy in autonomous car interior heating

Introduction. When driving cars in winter on intercity routes, problems may arise if the highways are covered with snow as a result of heavy snowfalls, which can last for several days or more, and movement during this period becomes impossible. An analysis of statistical data on weather events in winter has shown that in many regions, the closure of trails as a result of prolonged snowfalls can reach up to fifteen percent. In this case, an autonomous energy source, which it receives through the use of environmental properties, can provide significant assistance in improving the situation and increasing safety. The paper considers the technical solution and carries out a theoretical analysis of its characteristics aimed at solving the problem of autonomous power supply of the car interior in severe weather conditions.Materials and methods. The paper presents a technical solution and a theoretical analysis of its characteristics aimed at solving the problem of autonomous power supply of the car interior in severe weather conditions.Results. Based on the results of the information search, the design of a compact low-power wind power plant is proposed. The calculation of the heat loss of the car interior, the characteristics of a compact wind power plant was performed and the generated power required to heat the car interior in an extreme situation was determined.Discussion and conclusions. It has been established that the presented design of a low-power wind power plant can be used as a prototype for the development of an industrial design of an autonomous energy source for heating a car in extreme conditions. Similar installations can be used in other industries, for example, tourism, agriculture, and geological exploration.

Synoptic Conditions and Lake-to-Lake Connections for Days with Lake Effect on All of the Great Lakes

Abstract An investigation of lake effect (LE) and the associated synoptic environment is presented for days when all five lakes in the Great Lakes (GL) region had LE bands [five-lake days (5LDs)]. The study utilized an expanded database of observed LE clouds over the GL during 25 cold seasons (October–March) from 1997/98 to 2021/22. LE bands occurred on 2870 days (64% of all cold-season days). Nearly a third of all LE bands occurred during 5LDs, although 5LDs consisted of just 17.1% of LE days. A majority of 5LDs (56.5%) had lake-to-lake (L2L) bands, and these days comprised 43.5% of all L2L occurrences. 5LDs occurred with a mean of 26.1 (SD = 6.2) days per cold season until 2008/09 and then decreased to a mean of 13.8 (SD = 5.5) days during subsequent cold seasons. January and February had the largest number of consecutive LE days in the GL with a mean of 5.7 and 5.4 days, respectively. As the number of consecutive LE days increases, both the number of 5LDs and the occurrence of consecutive 5LD increase. This translates to an increased potential of heavy snowfall impacts in multiple, localized areas of the GL for extended time periods. The mean composite synoptic pattern of 5LDs exhibited characteristics consistent with lake-aggregate disturbances and showed similarity to synoptic patterns favorable for LE over one or two of the GL found by previous studies. The results demonstrate that several additional areas of the GL are often experiencing LE bands when a localized area has active LE bands occurring.

Development of YOLOv8 and Segment Anything Model Algorithm-Based Hanok Object Detection Model for Sustainable Maintenance of Hanok Architecture

A Hanok refers to a traditional Korean architectural structure. Construction structures undergo gradual, rather than instantaneous, transformations due to material degradation and deterioration in joint durability. Moreover, the detection of a structural problem by a nonexpert has severe implications for the safety of the structure. In particular, the precise effects of natural disasters, including storms, earthquakes, heavy snowfall, and structural defects, on structures are hard to determine. Additionally, manuals are limited by their reliance on quantitative assessments, which can pose difficulties for nonspecialists when it comes to recording numerical data. To solve this problem, 3D scanners have been widely employed in evaluating Hanoks, particularly those assigned as cultural heritage by the government. While those assigned as cultural heritage assets are systematically managed by experts and through budgets, the management system for Hanoks inhabited by the public has been overlooked. To fill this gap, this study focused on digital devices that are accessible to nonexperts as replacements for professional 3D scanners. Specifically, data from photos of a Hanok taken with smartphones were extracted to generate objective numerical data. AI training data for Hanoks were used to train the YOLOv8 algorithm and Segment Anything Model (SAM). The leaning values of columns, which constitute a fundamental structural component of a Hanok, were calculated using photographs that precisely captured the columns. The direction and distance of the column’s movement were extracted for visualization. To ensure the reliability of these values, the Hanok under investigation was 3D-scanned. Comparing the numerical values revealed a negligible margin of error, which confirmed the reliability of the photographic data values. Five-tier safety states (good, observation, caution, danger, and very dangerous) were defined based on the column movement distance by analyzing the real measurement data of government-managed Hanoks and used to visualize the structural condition of Hanoks. Therefore, nonexperts can determine the structural safety of a Hanok using objective numerical data, even in situations where its progressive deformation is not readily apparent. Objective numerical analysis based on reliably collected data allows nonexperts to accurately diagnose structural safety, thus facilitating prompt and suitable actions. The results of this study can serve to enhance the stability and longevity of Hanok structures, thus facilitating sustainable maintenance and management.

Towards a Model of Snow Accretion for Autonomous Vehicles

Snow accumulation on surfaces exposed to adverse weather conditions has been studied over the years due to a variety of problems observed in different industry sectors, such as aeronautics and wind and civil engineering. With the growing interest in autonomous vehicles (AVs), this concern extends to advanced driver-assistance systems (ADAS). Weather stressors, such as snow and icing, negatively influence the sensor functionality of AVs, and their autonomy is not guaranteed by manufacturers during episodes of intense weather precipitation. As a basis for mitigating the negative effects caused by heavy snowfall, models need to be developed to predict snow accumulation over critical surfaces of AVs. The present work proposes a framework for the study of snow accumulation on road vehicles. Existing icing and snow accretion models are reviewed, and adaptations for automotive applications are discussed. Based on the new capabilities developed by the Weather on Wheels (WoW) program at Ontario Tech University, a model architecture is proposed in order to progress toward adequate snow accretion predictions for autonomous vehicle operating conditions, and preliminary results are presented.

Seasonal changes in forest road use by mammals in a heavy snowfall area, north-eastern Japan: effects of management intensities

Seasonal changes in forest road use by mammals in a heavy snowfall area, north-eastern Japan: effects of management intensities

Diverse Characteristics of Extreme Orographic Snowfall Events in Little Cottonwood Canyon, Utah

Abstract Heavy orographic snowfall can disrupt transportation and threaten lives and property in mountainous regions but benefits water resources, winter sports, and tourism. Little Cottonwood Canyon (LCC) in northern Utah’s Wasatch Range is one of the snowiest locations in the interior western United States and frequently observes orographic snowfall extremes with threats to transportation, structures, and public safety due to storm-related avalanche hazards. Using manual new-snow and liquid precipitation equivalent (LPE) observations, ERA5 reanalyses, and operational radar data, this paper examines the characteristics of cool-season (October–April) 12-h snowfall extremes in upper LCC. The 12-h extremes, defined based on either 95th percentile new snow or LPE, occur for a wide range of crest-level flow directions. The distribution of LPE extremes is bimodal with maxima for south-southwest or north-northwest flow, whereas new-snow extremes occur most frequently during west-northwest flow, which features colder storms with higher snow-to-liquid ratios. Both snowfall and LPE extremes are produced by diverse synoptic patterns, including inland-penetrating or decaying atmospheric rivers from the south through northwest that avoid the southern high Sierra Nevada, frontal systems, post-cold-frontal northwesterly flow, south-southwesterly cold-core flow, and closed low pressure systems. Although often associated with heavy precipitation in other mountainous regions, the linkages between local integrated water vapor transport (IVT) and orographic precipitation extremes in LCC are relatively weak, and during post-cold-frontal northwesterly flow, highly localized and intense snowfall can occur despite low IVT. These results illustrate the remarkable diversity of storm characteristics producing orographic snowfall extremes at this interior continental mountain location. Significance Statement Little Cottonwood Canyon in northern Utah’s central Wasatch Range frequently experiences extreme snowfall events that pose threats to lives and property. In this study, we illustrate the large diversity of storm characteristics that produce this extreme snowfall. Meteorologists commonly use the amount of water vapor transport in the atmosphere to predict heavy mountain precipitation, but that metric has limited utility in Little Cottonwood Canyon where heavy snowfall can occur with lower values of such transport. Our results can aid weather forecasting in the central Wasatch Range and have implications for understanding precipitation processes in mountain ranges throughout the world.

Ocorrência de ondas de frio no Rio Grande do Sul no final do século XVIII e no século XIX e a produção do conhecimento climatológico

The state of Rio Grande do Sul experienced intense cold at the end of the 15th century and heavy snowfalls in the 19th century, specifically in 1858, 1862, 1867, 1870, 1871, 1873, 1875, 1879 and 1885. Due to the social problems caused by these severe weather events, the need arose to create a meteorological service. There were several initiatives in the 19th century, most notably the service organized by the Commissão de Obras do Porto de Rio Grande, the Public Works station in Porto Alegre and the Escola de Artes e Officio station in Pelotas.

Antarctic Snow Failure Mechanics: Analysis, Simulations, and Applications.

Snow failure is the process by which the stability of snow or snow-covered slopes is destroyed, resulting in the collapse or release of snow. Heavy snowfall, low temperatures, and volatile weather typically cause consequences in Antarctica, which can occur at different scales, from small, localized collapses to massive avalanches, and result in significant risk to human activities and infrastructures. Understanding snow damage is critical to assessing potential hazards associated with snow-covered terrain and implementing effective risk mitigation strategies. This review discusses the theoretical models and numerical simulation methods commonly used in Antarctic snow failure research. We focus on the various theoretical models proposed in the literature, including the fiber bundle model (FBM), discrete element model (DEM), cellular automata (CA) model, and continuous cavity-expansion penetration (CCEP) model. In addition, we overview some methods to acquire the three-dimensional solid models and the related advantages and disadvantages. Then, we discuss some critical numerical techniques used to simulate the snow failure process, such as the finite element method (FEM) and three-dimensional (3D) material point method (MPM), highlighting their features in capturing the complex behavior of snow failure. Eventually, different case studies and the experimental validation of these models and simulation methods in the context of Antarctic snow failure are presented, as well as the application of snow failure research to facility construction. This review provides a comprehensive analysis of snow properties, essential numerical simulation methods, and related applications to enhance our understanding of Antarctic snow failure, which offer valuable resources for designing and managing potential infrastructure in Antarctica.

Long-term changes in vegetation and land use in mountainous areas with heavy snowfalls in northern Japan: an 80-year comparison of vegetation maps

Comparison of old and new vegetation maps is an effective way to detect vegetation dynamics. Recent developments in computer technology have made it possible to accurately compare old paper vegetation maps with current digitized vegetation maps to reveal long-term vegetation dynamics. Recently, a 1:50,000 scale vegetation map of the Hakkoda Mountains in northern Japan, located in the ecotone of cool temperate and subalpine forests in northern Japan under an East Asian monsoon climate, from 1930 was discovered. We compared the 1930s vegetation map with the most recent 2010 vegetation map to test the following hypotheses: 1) the occurrence of upward expansion of the upper limit of cool-temperate deciduous forests, and 2) whether designation as a national park in 1936 would have reduced forestry and land use, expanded beech forests, and cool-temperate deciduous forests. To compare vegetation changes, 67 types of vegetation legends for the 1930 and 2010 maps were unified to 21 based on plant species composition. Consequently, vegetation has changed substantially over the past 80 years. 1) In the subalpine zone above 1,000 m, the coniferous forest area decreased by half. In the cool temperate zone below 1,000 m, the area of beech forests increased 1.48 times, and some of them could be shifted upwards, replacing subalpine fir forests in the lower part of the subalpine zone. 2) In areas below 700 m, deciduous oak forests once used as thickets were almost halved. Instead, climax and beech forests expanded. However, we also found that even after the area was declared a national park, oak forests were cleared and converted to commercial forests such as cedar plantations, cattle ranches, and horse pastures in some areas. These results will be useful for future ecosystem and biodiversity research/conservation and will provide baseline information for climate change adaptation policies.

Evaluation of Two Momentum Control Variable Schemes in Radar Data Assimilation and Their Impact on the Analysis and Forecast of a Snowfall Case in Central and Eastern China

To evaluate the impact of different momentum control variable (CV) schemes (CV5, the momentum control variable option with ψχ and CV7, the momentum control variable option with UV) on radar data assimilation (DA) in weather research and forecasting model data-assimilation (WRFDA) systems, a heavy snowfall in central and eastern regions of China, which started on 6 February 2022, was taken as a case in this study. The results of the wind-field increments from the single observation tests indicated that the wind-field increments had a larger range of influence when stream function and velocity potential (ψχ) were used as momentum control variables in CV5. Some spurious increments were also generated in the wind-field analysis, since CV5 tended to maintain the integrated value of the wind field. When U-wind and V-wind were used as control variables in CV7, the wind-field increments had a smaller impact range, and there was less dependence among different locations on the wind increments. For the heavy snow case, the CV7 schemes displayed some improvements in simulating the composite reflectivity compared to the other two experiments, since the composite reflectivity in the CV5 and control experiments were overestimated to some level. It was also found that the RMSEs were lower in the CV7 compared to those in the CV5 in the short-term forecasts during the data-assimilation cycles. Results also indicated that the CV7 had a more significant effect on the 6 h accumulated precipitation forecasts. Meanwhile, the experiment Exp_CV7 achieved the best ETS and FSS scores among the three groups of experiments, while Exp_CV5 appeared to be generally superior to the CTRL. In summary, the precipitation of Exp_CV7 yielded the rainfall intensity and location most close to the observation compared to those from both the CTRL and Exp_CV5 experiments.

The influence of Urmia Lake desiccation on an extreme snowfall event: A case study using the WRF-Lake model

Urmia Lake in the northwest Iran has undergone significant desiccation in recent decades, shrinking to a much smaller size than its original. This study implements the WRF-Lake coupled modeling system to examine the impacts of the observed physical changes over the Lake during a heavy snowfall event occurred February 1–2, 2017. Modifications are introduced to represent the shrunken, salt-encrusted lake surface and surrounding arid environment through updating land use, soil characteristics, lake size and depth, and albedo data. Experiments incorporating lake-atmosphere coupling and land surface refinements (Lake_CTL, Lake_LULC, Lake_AL) outperform simulations without a lake (WRF_noLake) in reproducing observed precipitation patterns at stations surrounding the Urmia Lake. All simulations underestimate the extreme 290 mm snowfall recorded at the Urmia station; however, the Lake_LULC and Lake_AL improve snowfall representation at other stations around the lake. Regarding snow cover validation, Lake_LULC and Lake_AL exhibit a higher correlation with satellite observations and show a significant improvement in snow detection near Urmia Lake. They have a Probability of Detection (POD) of 0.94 and a Critical Success Index (CSI) of 0.91, compared to a POD of 0.79–0.80 and a CSIs of ∼0.77 for the default lake model (Lake_CTL) and the WRF_noLake experiments, respectively. Additionally, adjusting the initial lake surface temperature to match observations substantially reduces cold biases in near-surface air temperatures over Urmia Lake. The lack of lake temperature updating in WRF-Lake poses ongoing challenges in the model, though. In summary, this study demonstrates that refining the representations of desiccated lakes and their surroundings in high-resolution coupled models would improve simulations of meteorological processes influenced by lake-atmosphere interactions.

Intraspecific genetic divergence of the subalpine shrubby variety Quercus crispula var. horikawae from the mountain tree variety Q. crispula var. crispula in Japan

AbstractEcotypic divergence in tree taxa often occurs in subalpine habitats, where environmental conditions are more stressful than those in lower elevations. In the white oak species in Japan Quercus crispula, the subalpine shrubby variety Q. crispula var. horikawae (Qch) has been recognized in central and northern Honshu. Although Qch has different phenotypes from Q. crispula var. crispula (Qcc), genetic divergence between Qcc and Qch has not been examined yet. Pairs of Qcc and Qch populations in eight locations and additional Qcc and Qch populations around these locations were investigated. Leaf size of Qch was smaller than that of Qcc. Chloroplast DNA haplotypes were shared between the Qcc and Qch populations. In genotypes at 29 nuclear microsatellite loci, genetic diversity did not differ between the Qcc and Qch populations. Principal component analysis and a neighbor‐joining tree of populations based on the genotypes demonstrated that 13 Qcc populations and eight Qch populations were grouped separately, except for three Qch populations that were grouped to Qcc. Climatic conditions in the eight Qch populations were characterized by lower temperature and heavier snowfall than those in the 16 populations of the genetic group of Qcc. These results suggest genetic divergence between Qcc and Qch associated with subalpine climatic conditions, irrespective of leaf size. The origin of the subalpine Qch lineage and the history of ecotypic divergence should be investigated in future genomic studies.

Decreasing trends of mean and extreme snowfall in High Mountain Asia

Intense warming profoundly alters precipitation phase patterns and intensity in High Mountain Asia (HMA). While snowfall climatology and precipitation extremes have been studied, there is a lack of understanding of snowfall extremes within HMA. Here, we investigate the spatial and temporal variability of non-extreme and extreme snowfall in hydrological years 1979–2020 using multi-source meteorological data, compare weather systems during extreme and non-extreme snowfall events, and identify key circulation factors that influence fluctuations in mean annual snowfall and extreme snowfall. The snowfall amount (−0.13 d/mm), days (−0.56 d/a), and fraction (−0.0012) were significantly reduced in HMA, with a shorter snowfall season (−0.52 d/a). Some extreme snowfall metrics (maximum 1-day snowfall and maximum 3-day snowfall) were insensitive to climate change, whereas the maximum consecutive snowfall days (−0.007 d/a), snowfall amount (−0.0023 mm/a), heavy snowfall days (S95pD; 0.0087 d/a), and extremely heavy snowfall days (S99pD; −0.1019 d/a) showed significant decreases. Synthetic analyses show that extreme snowfall events were more likely to occur within a narrow temperature range (−5 °C to 3 °C) with higher relative humidity and precipitation compared to non-extreme events. A stepwise regression method was used to determine that the fluctuation in the average annual snowfall was closely related to the Atlantic Multidecadal Oscillation, whereas the variation in extreme snowfall was mainly influenced by the Southern Oscillation Index. Our research provides a reference for assessing the potential impacts of climate change on a regional scale for risk management and disaster adaptation.

Stratospheric influence on the winter North Atlantic storm track in subseasonal reforecasts

Abstract. Extreme stratospheric polar vortex events, such as sudden stratospheric warmings (SSWs) or extremely strong polar vortex events, can have a significant impact on surface weather in winter. SSWs are most often associated with negative North Atlantic Oscillation (NAO) conditions, cold air outbreaks in the Arctic and a southward-shifted midlatitude storm track in the North Atlantic, while strong polar vortex events tend to be followed by a positive phase of the NAO, relatively warm conditions in the extratropics and a poleward-shifted storm track. Such changes in the storm track position and associated extratropical cyclone frequency over the North Atlantic and Europe can increase the risk of extreme windstorm, flooding or heavy snowfall over populated regions. Skillful predictions of the downward impact of stratospheric polar vortex extremes can therefore improve the predictability of extratropical winter storms on subseasonal timescales. However, there exists a strong inter-event variability in these downward impacts on the tropospheric storm track. Using ECMWF reanalysis data and reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project database, we investigate the stratospheric influence on extratropical cyclones, identified with a cyclone detection algorithm. Following SSWs, there is an equatorward shift in cyclone frequency over the North Atlantic and Europe in reforecasts, and the opposite response is observed after strong polar vortex events, consistent with the response in reanalysis. However, although the response of cyclone frequency following SSWs with a canonical surface impact is typically captured well during weeks 1–4, less than 25 % of the reforecasts manage to capture the response following SSWs with a “non-canonical” impact. This suggests a possible overconfidence in the reforecasts with respect to reanalysis in predicting the canonical response after SSWs, although it only occurs in about two-thirds of the events. The cyclone forecasts following strong polar vortex events are generally more successful. Understanding the role of the stratosphere in subseasonal variability and predictability of storm tracks during winter can provide a key for reliable forecasts of midlatitude storms and their surface impacts.

시스템 다이내믹스를 이용한 재난관리자원의 동원 및 응원 시뮬레이션

In this study, a disaster management resource simulation model between local governments was suggested using the system dynamics methodology. At this time, the subject of analysis was selected as the stockpile of deicing agents for each district in Seoul. Based on statistical data, a scenario was set in which the expected amount of heavy snowfall reached level 3, and the response to heavy snowfall reached level 4 for 5 days in the 11 districts of Seoul. The simulation shows that in areas with heavy snow, the deicing agent stock falls below the minimum by the 4.5th day, and in areas without heavy snow, it happens by the 14.5th day. Additionally, it was estimated that the Seoul Disaster Resource Integrated Management Center required about 2,155 tons of deicing agents. This study is meaningful because it can be used to set the RTO(Recovery Time Objective) for the mobilization and support of deicing agents. Furthermore, it can be used to interconnect disaster management resources by suggesting an appropriate stockpile of deicing agents for the Seoul Disaster Resource Integrated Management Center.

Heavy snowfall has already been enhanced by anthropogenic global warming in Japan

Heavy snowfall has already been enhanced by anthropogenic global warming in Japan

Tragedies due to human folly: A learnt lesson from year 2023 Sikkim avalanche

In the Himalaya, natural hazards are common and their places, as well as timings, are difficult to predict. Rough terrain, unpredictable weather conditions and poor road networks of the Himalaya make the mitigation measures even more challenging as well as difficult. On April 4, 2023, 7 people have died and 13 were injured due to a small loose snow avalanche at 14th Mile, on the way to Nathula Pass, near Indo-China Border in Sikkim state (India). State government had advised the people, not to visit the area due to the poor weather conditions. However, people have ignored this precious advice and lost their lives. Along with Indo-China border, the region is famous for its scenic beauty. In addition, the region is one of the most difficult border areas with the limited infrastructural development for armed forces. The present study explores the causes and consequences of this killer avalanche from an environmental determinism. The results suggest that, due to the western disturbances, the region received heavy snowfall. Higher average temperature and flowing stream beneath snow, make the snow unstable. Weight of humans put pressure on the weak snow which led to formation of avalanche. Our finding suggests that peoples’ negligence about government advices have led to casualties. The purpose of this case study is to highlight the human folly which invites such unfortunate incidences across the globe. Further, this study emphasis the need of public-government partnership to minimize the impact of such upcoming hazards.