Snow Damage Research Articles

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.

Regional variation in branch morphological and physical traits in Abies sachalinensis associated with winter climatic conditions

ABSTRACT In boreal forests, overwintering strategies, including snow adaptation, can be expected in the branches of coniferous trees. Although it is difficult to detect resistance to snow disturbances and few studies have quantified the functional traits related to snowfall resistance, the regional variation associated with winter conditions may provide cues for identifying adaptive traits against snow damage. In common garden trials, we examined the regional variation in branch functional traits in Abies sachalinensis, one of the most important conifers in boreal forest silviculture. Morphological traits reflecting susceptibility to snow accumulation, such as branch estimated area, Young’s modulus and flexural rigidity, which represent deformability to snow loads, and base diameter and xylem density were evaluated in five-year-old branches from eight provenances. The length and estimated area of a branch showed regional variation. Significant regional variation in flexural rigidity rather than Young’s modulus and no significant correlation between xylem density and mechanical traits indicated that the physical properties of branches depend on their size rather than on their materials. A significant correlation with winter climatic conditions was observed between flexural rigidity and total monthly precipitation in January. Our results showed that the thicker the branch is, the larger the branch area and the more rigid it is. This indicates that the branch size-dependent resistance to snow loads might be associated with winter adaptation to local snowfall conditions.

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.

Predicting snow damage in conifer forests using a mechanistic snow damage model and high-resolution snow accumulation data

ABSTRACT Forest damage caused by heavy wet snow accumulation in the canopy is the second most important abiotic forest disturbance agent in Nordic conifer stands after wind. The extent and frequency of snow damage in the future climate in the Nordic region is a major uncertainty. Few mechanistic models of snow damage risk to trees exist that could support forest management scenario analysis and decision making. We propose a snow damage risk model consisting of a numerical weather prediction-based snow accumulation model for forest canopies and a mechanistic critical snow load model. Snow damage probability predictions were validated on snow breakage data from the winters of 2016 and 2018 covering 3.5 million individual trees in south-eastern Norway derived from pre- and post-damage aerial laser scanning campaigns. The proposed model demonstrated satisfactory damage and no-damage class separation with an AUC of 0.72 and 0.77 in Norway spruce and Scots pine, respectively, and an F1 score of 0.7 in conifers taller than 10 m that suffered moderate stem breakage. The model achieved a classification accuracy that is comparable to that of statistical models but is simpler and requires fewer inputs.

Productivity analysis and costs of wheel cable skidder during salvage logging in European beech stand

Salvage logging is increasing in Central Europe because of the growth of severe meteorological events, and timber harvesting in these conditions is challenging in terms of both productivity performances and safety of the operations. In recent years, with the increase of natural calamities, several researchers studied machinery productivity performances regarding salvage logging carried out by ground-based systems. In fact, a common post-disturbance management approach is salvage logging which consists of the widespread removal of damaged trees. In this research, system productivity and the cost of salvage logging are analysed in European beech stands affected by wet snow. The accretion of heavy wet snow poses the greatest risk to forests in the Northern Hemisphere. This type of snow attaches more effectively to tree crowns and branches when temperatures are close to freezing at the time of precipitation. As a result, trees may break or bend and may be uprooted when the soil is unfrozen. This study has been implemented to evaluate the productivity and cost-effectiveness of extraction in salvage logging deployed with a skidder in beech stands affected by two different types of wet snow damage. The results show that the productivity of the four-wheel-drive cable skidder, despite operating in salvage cutting with a removal intensity of 10%, is 14.73 m3·SMH–1, similar to skidder performances in ‘ordinary’ cuttings. Skidder’s productive time was 86% of the scheduled time, whereas the delays were due to organisational reasons, mechanical delays, and adverse weather conditions. The mean travel speed of the cable skidder obtained in this study is close to the results obtained from other studies on similar machines. The costs per unit are lower than effective cost consumptions for the other cable skidders and agricultural tractors, adapted for skidding operated in hardwood salvage logging. Therefore, under the given conditions, the operation of the fourwheel-drive cable skidder is viable from a silvicultural, technical, and economic point of view in the salvage operation logging.

Field observations of wet snow accretion on overhead transmission lines at the Kushiro test line

Wet snow accretion on overhead lines often causes large-scale snow damage. The Kushiro test line, Japan, was constructed in 2013 for field observations of wet snow accretion in conductors and insulators as well as galloping resulting from snow accretion in overhead lines. Several cases of noticeable wet snow accretion have been observed at this site, especially in the winter of 2014. In the most notorious case, more than 2 kg/m of snow was found to be accreted on the ACSR240 single conductor, which has a diameter of 22.4 mm. In the case of single conductors without countermeasures, snow accretion developed to form a cylindrical sleeve with wire rotation. However, in some cases, the cylindrical sleeve of snow accretion also occurred in four-bundled conductors, where line spacers prevent the wire from rotating. This indicates that the accreted wet snow slides along the strands of the wire to form a cylindrical sleeve. Accordingly, the effectiveness of the snow resistance ring, which is the most commonly used anti-snow-damage device in Japan, in preventing snow accretion was confirmed. As the ring prevents the accreted snow from sliding, accretion areas tend to split, causing the snow to be shed from the conductor. More heat transfer from the air to snow and heat generated by the electric current facilitated the sliding of snow along the strand, making the snow resistance ring more effective.

Snow damage in a Himalayan forest maintains the dominance of evergreen oaks

AbstractExtreme climate events are increasing due to climate change. Heavy snowfall in a Himalayan evergreen oak forest damaged 21% trees and created large treefall gaps. Such occasional events may help define the upper altitudinal boundary of certain species. Oak trees with branches lopped by local communities showed lower damage.

Climate suitability division of solar greenhouse in Inner Mongolia Autonomous Region, China.

To resolve the issue of scientific planning and rational layout of different vegetable greenhouses in Inner Mongolia Autonomous Region, we selected the days of low temperature in winter, sunshine hours, overcast days, extreme minimum temperature, days of monsoon disaster, days of snow cover in production season of greenhouse as the climate zoning indicators, based on ground-based observation data from 119 meteorological stations (1991 to 2020) and the growing demand of leafy and fruity vegetables in greenhouse, combined with the analysis of key meteorological factors in production season and the study of meteorological disaster indicators such as low temperature and cold damage, wind disaster, snow disaster. We analyzed the indices, classification and division of comprehensive climate suitability zoning of leafy and fruity vegetables at various slopes (35°, 40°) of solar greenhouse by the weighted sum method. The results showed that the climatic suitability zoning grades of leafy and fruity vegetables at 35° and 40° slope of greenhouse was highly consistent, and that the greenhouse climate suitability of leafy vegetables was higher than that of fruity vegetables in the same region. As the slope increased, wind disaster index decreased and snow disaster index increased. Climate suitability was different in areas affected by wind and snow disasters. The northeast of the study area was mainly affected by snow disasters, and the climate suitability of 40° slope was higher than 35°. The southeast of the study area was mainly affected by wind disasters, and the climate suitability of 35° slope was higher than 40°. Alxa League, Hetao Irrigation District, Tumochuan Plain, most parts of Ordos, southeast of Yanshan foothills and the south of West Liaohe Plain were the most suitable area for the solar greenhouse, because they had the suitable solar and hot resources and the low risks of wind and snow damage, which were also the key development areas of current and future facility agriculture. Due to the deficiency of solar and hot resources, high energy consumption in greenhouse production and frequent snow storms, the area around Khingan range in the northeast of Inner Mongolia was unsuitable for greenhouse.

Application of Remote Sensing Data for Assessment of Bark Beetle Attacks in Pine Plantations in Kirkovo Region, the Eastern Rhodopes

Intensive forest afforestation with native pine species was developed in the 1960s on degraded and deforested lands in the region of the Eastern Rhodopes (south-eastern Bulgaria). Severe damage by wet snow was registered in the coniferous forests of the Rhodopes in March 2015. In the following years, bark beetle attacks were registered on the broken and felled fresh wood. As a result, bark beetle infestation spots appeared in the pine plantations. In the period 2019–2021, damage caused by bark beetles was assessed in the region of State Forestry Kirkovo (the Eastern Rhodopes, south-eastern Bulgaria). An integrated approach using the data of the information system of the Executive Forest Agency (ISEFA), remote sensing data obtained by an “eBee SQ” unmanned aerial vehicle (UAV) equipped with a “Parrot Sequoia” multispectral camera, and subsequent terrestrial observations, was applied. ISEFA data showed that there was no serious damage caused by abiotic and biotic factors in the pine forests of SF Kirkovo until 2014. Snow damage in 2015 affected 513 ha of pine plantations, and bark beetle infestations reached up to 1316 ha in 2016. In 2019, a total of 226.87 ha of pine plantations were captured in three localities—Fotinovo, Kirkovo, and Kremen. The relative share of damage caused by bark beetles was greater in P. sylvestris plantations (15.3–23.0%), compared to damage in P. nigra (2.3%). Four different categories of normalised difference vegetation index (NDVI) were separated in bark beetle infestation spots—living trees, dead trees, grass and shrub vegetation, stones and rocks. The NDVI values in locations with living trees varied between 0.500 (spaces between tree crowns) and 0.700 (central part of the crown projection) (an average of 0.617). In the locations with dead trees, the average values of NDVI of lying trees was 0.273, and in standing trees, NDVI varied between 0.275 (central part of crown projections) and 0.424 (spaces between tree crowns). In the locations with grass and shrub vegetation, stones and rocks, the average NDVI was 0.436 and 0.329, respectively. In the field study, average defoliation of 31.2–32.3% was registered in P. sylvestris plantations, and 47.4% in P. nigra plantations. Defoliations mainly were caused by pine processionary moth (Thaumetopoea pityocampa) and fungal pathogens (Dothistroma septosporum and Lecanosticta acicola). The damage was caused by Ips acuminatus (in P. sylvestris only), and I. sexdentatus, Tomicus piniperda and T. minor (in P. sylvestris and P. nigra). Infestations by other xylophages, such as Phaenops cyanea, Rhagium inquisitor, and Pissodes spp., were also found on pine stems.

Evaluation of Drifting Snow Susceptibility Based on GIS and GA-BP Algorithms

Drifting snow, the flow of dispersed snow particles near ground level under the action of wind, is a major form of snow damage. When drifting snow occurs on railways, highways, and other transportation lines, it seriously affects their operational safety and results in drifting snow disasters. Drifting snow disasters frequently occur in the high latitudes of northwest China. At present, most scholars are committed to studying the prevention and control measures of drifting snow, but the prerequisite for prevention is to effectively evaluate the susceptibility of drifting snow along railways and highways to identify areas with a high risk of occurrence. Taking the Xinjiang Afukuzhun Railway as an example, this study uses a geographic information system (GIS) combined with on-site monitoring and surveys to establish a drifting snow susceptibility evaluation index system. The drifting snow susceptibility index (DSSI) is calculated through the weight of an evidence (WOE) model, and a genetic algorithm backpropagation (GA-BP) algorithm is used to obtain optimised evaluation index weights to improve the accuracy of model evaluation. The results show that the accuracies of the WOE model, WOE backpropagation (WOE-BP) model, and weight of evidence genetic algorithm backpropagation (WOE-GA-BP) model are 0.747, 0.748, and 0.785, respectively, indicating that the method can be effectively applied to evaluate drifting snow susceptibility.

Snow damage in plantations of Austrian pine and Scots pine in the western part of the Republic of Srpska

The paper presents research related to the harmful effects of snowfall in Austrian pine and Scots pine plantations in the western part of the Republic of Srpska. The most common types of tree damage are snow break and snowfall. The harmful effect of snow is more present in Scots pine plantations and at lower altitudes. Also, the harmful effect of snow is reflected in the reduction of the vitality of damaged trees, which become suitable for the attack of other harmful biotic factors, primarily harmful insects and fungi.

Ground- and Satellite-Based Evaluation of WRF Snowfall Prediction

This study performed 4-day numerical integration in 1-hour intervals using the Weather Research and Forecasting (WRF) model for four major cases of heavy snowfall that occurred from 2020 to 2021. The model-predicted snow depth data were compared with the ground-observed snow depth and the satellite-observed snow cover data and then were statistically verified. The scalar verification results for ground data from the four cases showed a root–mean–square error of 2.55-16.67 cm and a correlation coefficient of 0.48-0.80, whereas the verification results with satellite data showed the correlation coefficients of 0.38-0.60. For categorical verification, using a threshold value of a snow depth exceeding 5 cm, the proportion correct was 90% or higher for ground observations of each case. In addition, in the satellite categorical verification, when the threshold value of the Snow Cover Fraction (SCF) exceeds 0.5, the proportion correct was 50% or more. These results are meaningful because the model snow depth verification methods were devised strategically for the first time using both the snow depth data of the mesoscale ground observation networks and ultra-high-resolution Sentinel-2 satellite data currently available in Korea. The findings of this study will contribute to the development of a high-resolution numerical prediction model and its verification methodology for snowfalls in the Korean Peninsula, eventually leading to increased prediction accuracy and reduced snow damage.

The effect of snow damage on self-organization in a primary subtropical evergreen broadleaved forest in Southwest China

At the beginning of 2015, a primary subtropical evergreen broadleaved forest in Southwest China experienced an extreme snow anomaly. We used a thermodynamic approach to evaluate the self-organization of the forest in response to snow disturbance. We found that the snow disturbance induced severe vegetation damage, as indicated by LAI significantly decreased by 33.19% and 40.85% in 2015 than the pre-disturbance years (2013–2014), respectively. The forest had the higher self-organization in 2015 with the higher ability of capture exergy (Rn/DR) and dissipation exergy (TRNc). The changes in vegetation patterns of the primary subtropical evergreen forest enhanced the ecosystem self-organization. Our finding was inconsistent with the general theory that the disturbance of natural systems reduces exergy capture ability and increases exergy dissipation.

Mapping the probability of forest snow disturbances in Finland.

The changing forest disturbance regimes emphasize the need for improved damage risk information. Here, our aim was to (1) improve the current understanding of snow damage risks by assessing the importance of abiotic factors, particularly the modelled snow load on trees, versus forest properties in predicting the probability of snow damage, (2) produce a snow damage probability map for Finland. We also compared the results for winters with typical snow load conditions and a winter with exceptionally heavy snow loads. To do this, we used damage observations from the Finnish national forest inventory (NFI) to create a statistical snow damage occurrence model, spatial data layers from different sources to use the model to predict the damage probability for the whole country in 16 x 16 m resolution. Snow damage reports from forest owners were used for testing the final map. Our results showed that best results were obtained when both abiotic and forest variables were included in the model. However, in the case of the high snow load winter, the model with only abiotic predictors performed nearly as well as the full model and the ability of the models to identify the snow damaged stands was higher than in other years. The results showed patterns of forest adaptation to high snow loads, as spruce stands in the north were less susceptible to damage than in southern areas and long-term snow load reduced the damage probability. The model and the derived wall-to-wall map were able to discriminate damage from no-damage cases on a good level (AUC > 0.7). The damage probability mapping approach identifies the drivers of snow disturbances across forest landscapes and can be used to spatially estimate the current and future disturbance probabilities in forests, informing practical forestry and decision-making and supporting the adaptation to the changing disturbance regimes.

Change of the distribution and management of avalanche prevention forest in Taira, Gokayama, Japan

Located in Gokayama, Toyama Prefecture, Japan, Taira is a village with precipitous terrain and heavy snowfall, thus causing snow damage to occur frequently. In order to protect the village from disasters caused by avalanches, avalanche prevention forests (APF) has been maintained and managed since 1600s. However, due to the decline and aging of the rural population in mountainous areas in recent years, APF maintenance has become an important issue. Thus, this study aims to provide basic data for searching maintenance methods for APF, by clarifying the distribution, forest stand, management, and those changes. As a result, the distribution of APF changed after 1940s. To be specific, the APF distributed on the steep slopes behind each village has been preserved, while the conventional functions have been taken over. However, there has been still increasing the number of planted forests, scatter, and grass in the APF, which made the distribution of conventional APF decreased. In terms of the forestry laws, lots of parts of APF were designated according to the Protection Forest System of Forest Act step by step, which provides legal protection for part of APF. In short, it’s important to integrate sustainable maintenance solutions from multiple perspectives (disaster prevention, ecosystems, and landscapes, etc.) and to adjust measures according to different conditions of conventional APF and the part being protected by law.

Influence of Different Thinning Treatments on Stand Resistance to Snow and Wind in Loblolly Pine (Pinus taeda L.) Coastal Plantations of Northern Iran

Loblolly pine (Pinus taeda L.) is one of the main exotic conifer species that has been widely planted for the past fifty years for timber production in the coastal areas of northern Iran. Heavy snowfall and strong winds can cause much damage to these forests over a short time span of only a few years. This study was conducted to estimate snow and wind damage and analyze the role of stand thinning in their resistance to snow and wind. Amount and type of snow and wind damage were examined through systematic (80 m × 80 m) sample plots (each plot area of 625 m2) in nine different stands (2–10 plots in each stand) in terms of age, structure, and silviculture history in three replications for each stand in April and May 2020. Results showed that the amount of snow and wind damage had a wide range from 1.3% to 30.7%. Snow damage was more than three times that of wind. Snow and wind damage in the young stands were significantly more serious (p < 0.01) than in the middle-aged and old stands, and damage was significantly higher (p < 0.01) in the unthinned stands than in the thinned ones. Slenderness coefficient (Height/Diameter ratio, HD ratio) of trees resulted to be a good indicator in young and middle-aged stands, while crown form indices (relative crown length and relative crown width) were acceptable indicators in old stands for risk of snow and wind damage. Our results showed that the normal thinning (15% of basal area) decreased snow and wind damage in all the stands, while the heavy thinning (35% of basal area) reduced the snow damage, but it increased the wind one. It is possible to recommend high intensity thinning in young stands, normal thinning in middle-aged stands, and light thinning (15% of basal area) in old ones.

Climate change induces multiple risks to boreal forests and forestry in Finland: A literature review.

Climate change induces multiple abiotic and biotic risks to forests and forestry. Risks in different spatial and temporal scales must be considered to ensure preconditions for sustainable multifunctional management of forests for different ecosystem services. For this purpose, the present review article summarizes the most recent findings on major abiotic and biotic risks to boreal forests in Finland under the current and changing climate, with the focus on windstorms, heavy snow loading, drought and forest fires and major insect pests and pathogens of trees. In general, the forest growth is projected to increase mainly in northern Finland. In the south, the growing conditions may become suboptimal, particularly for Norway spruce. Although the wind climate does not change remarkably, wind damage risk will increase especially in the south, because of the shortening of the soil frost period. The risk of snow damage is anticipated to increase in the north and decrease in the south. Increasing drought in summer will boost the risk of large‐scale forest fires. Also, the warmer climate increases the risk of bark beetle outbreaks and the wood decay by Heterobasidion root rot in coniferous forests. The probability of detrimental cascading events, such as those caused by a large‐scale wind damage followed by a widespread bark beetle outbreak, will increase remarkably in the future. Therefore, the simultaneous consideration of the biotic and abiotic risks is essential.

Evaluation for characteristics of wet snow accretion on transmission lines - Establishment of an experimental method using a vertical plate

It is of primary importance to decrease risks of snow damages on electric transmission lines to ensure a stable supply of electricity. For the acquisition of reliable data to clarify characteristics of snow accreted on a transmission line, it is effective to conduct an indoor experiment. The present report describes an experimental method for snow accretion on a vertical plate by using a wind tunnel including measurements of snow properties such as snow density and liquid water content, which can be extended to measure a shear adhesive stress of accreted snow. These measurements are systematically applied to different conditions of the wind speed, the air temperature, as well as the snow flux particularly focusing on wet snow accretion. Based on measurement data, the influence of the wind speed, the air temperature, and the snow flux on the snow density and the liquid water content is investigated. The analysis is further extended to include a parametric evaluation of the snow density and the liquid water content to examine their dependency on atmospheric parameters. As a result of analytical considerations of the consolidation effect by wind and melting of snow by heat exchanges, equations to estimate the snow density and the liquid water content by the wind speed, the air temperature, as well as the accreted snow mass are derived. The experimental results validate these equations, which enable the acquisition of desired snow properties by adjusting experimental parameters. The successful evaluation of snow properties in the presented experiments can substantially contribute to the establishment of practical data-set for further investigations to characterize wet snow accretion.

Mitigation of Snow Damage in Gangwon by Analyzing Snow Density and Characteristics

Over the past 20 years, Korea has been endlessly damaged by heavy snow. The damages are significantly different between regions, and therefore, it is important to analyze and understand the characteristics of greenhouse and snowfall in those regions. Snow weight, which affects the amount of damage, varies up to seven times depending on temperature and humidity, in addition to the amount of snow. Therefore, in this study, snow depth and density were compared to determine a solution. Based on a disaster report and the data collected from the Korea Meteorological Administration, field surveys were conducted by classifying four administrative districts in Gangwon containing multiple greenhouses, and snowy areas were selected and sampled. Finally, the density of snow and snow depth were measured. The snow density increased proportionally to its depth. The results of this study could help provide solutions to snow damage in the future. Keywords: Snow Damage, Amount of Snow, Snow Density

Snow Vulnerability Analysis for Administrative Districts in South Korea

Extreme weather events, such as summer typhoons, heavy rain, cold weather, and heavy snow, are on the rise. Heavy rain, typhoons, and heavy snow are the main natural disasters that cause severe damage in South Korea. Recently, there has been an increase in the direct and indirect damages caused by heavy snow. However, studies on heavy snow damage are scarce; research on major natural disasters in Korea has generally focused on heavy rain and typhoons, which occur in the summer. In this study, 229 cities and districts vulnerable to snow damage were analyzed. The pressure-state-response structure was used for the vulnerability analysis, and the factors influencing heavy snow damage were classified as pressure, state, and response index. Entropy theory, which is easy to apply and excludes subjective judgment, was used to calculate the weight of each factor. As a result, the ranking of areas vulnerable to heavy snow damage was compiled. These results demonstrate the potential for the implementation of the heavy snow disaster prevention project. Keywords: Entropy, Heavy Snow Damage, PSR, Snow Vulnerability Index