Snow Damage Research Articles

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

A Study on Storm and Flood Insurance Management Mapping: Case Study of Incheon Metropolitan City

In this research, we have used spatial information analysis techniques and procedures to process storm, flood, and snow damage risks, and apply premium rates to produce a Storm and Flood Insurance Management Map. To calculate risk, we used ArcGIS’s main features to overlay, integrate, and classify data. Moreover, we designed an ArcGIS Model Builder program to process very large amounts of risk data quickly and accurately. Excel’s pivot feature was used to calculate areas and premium rates according to flood depth. In the case of Incheon metropolitan city, the average risk was 2.85 on the 4-level scale, which lies between "alert" and "danger" and corresponds to the 1st of the 4 premium rate grades. In particular, there were high risks and high premium rates in areas around ports, ocean beaches, and beaches connected to rivers. We expect that this insurance management map created using spatial information analysis techniques will provide useful data for scientific natural disaster response and prevention planning, rational insurance rate calculation and application, and promotion of policies, which identify and prevent areas at risk for frequent storm and flood damage.

자연재해 종별 학교시설물의 피해유형분석 및 개선방안 : 2014년 ~ 2016년간의 피해정보를 중심으로

Purpose: It is impossible to defend against natural disasters artificially, but they can be prevented or minimized in advance, by mapping out preventive measures, such as design and management, methods and construction of facilities, which consider the size of external force. School facilities have been constructed in large quantities to accommodate the rapidly increasing number of students after the 1952 implementation of compulsory education. For this reason, it is unavoidable that the ongoing deterioration of buildings is connected to natural disasters, which lead to considerable damage. However, it is not easy to improve facilities, since most of school damage data regarding the scale and degree of damage are not disclosed and there is a shortage of data to analyse. Method: This study made an investigation of Types for damage from natural disasters, such as wind damage, flood damage, snow damage, lighting damage, and areas vulnerable to natural disasters, based on the school facilities of 17 cities and provinces all over the country for 3 years from 2014 to 2016, and classified major damaged facilities according to the names of school buildings. Result: Improvement plans for reducing damage of major areas and particular facilities from natural disasters were suggested for different types of disasters.

昭和20年（1945）の高田（上越市）における豪雪と雪害

昭和20年（1945）の高田（上越市）における豪雪と雪害

Modelling damage occurrence by snow and wind in forest ecosystems

Snow and wind damages are one of the major abiotic disturbances playing a major role in forest ecosystems and affecting both stand dynamics and forest management decisions. This study analyses the occurrence of wind and snow damage on Norwegian forests, based on data from four consecutive forest inventories (1995–2014). The methodological approach is based on boosted regression trees, a machine learning method aiming to demonstrate the effects of different variables on damage probability and their interactions as well as to spatialize damage occurrence to make predictions. In total, 313 models are fitted to detect trends, interactions and effects among the variables. The main variables associated with damage occurrence are consistent across all the models and include: latitude, altitude and slope (related to site and location), and tree density, mean diameter and height (related to forest characteristics). The results show that stand dominant height is a key variable in explaining damage probability, whereas stand slenderness has a limited effect. More heterogeneous forest structures make birch dominated stands more resistant to damage. Finally, the models are translated into occurrence maps, to provide landscape-level information on snow and wind damage hazard. Further application of the models can be oriented towards assessing the probability of damage for alternate stand management scenarios.

Simulating the effects of wind and snow damage on the optimal management of Norwegian spruce forests

AbstractOverlooking the risk of wind and snow damage in forest planning may lead to suboptimal management prescriptions. In this study, we analysed the optimal management of an even-aged, spruce-dominated stand in Norway under the risk of snow and wind damage. The management aim was to maximize discounted net revenues of timber production. We used a simulation-optimization system based on models for stand dynamics and damage, using either deterministic or stochastic approach to consider risk. The different approaches to simulating damage resulted in 41 optimization cases. The results show that considering risk leads to earlier cuttings, lower growing stock densities towards the end of the rotations and changes in the number and intensity of thinnings. The inclusion of stochastic damage provided a valid approach for considering the uncertainty associated with the risk of damage. Ignoring the effect of wind and snow damage in the calculations resulted in up to 25 per cent overestimation of the revenues. The results from this study will help to integrate the risk of natural disturbances into forestry decision-making, and provide a better understanding of the implications that snow and wind damage have on optimal forest management.

Determination of Some Stand and Site Parameters Influencing the Snow Damage in Trees: A Case Study of Boyabat Forest Enterprise, Turkey

Aim of Study: Determination of the snow damage triggering stand and site parameter on trees.Area of Study: Damaged compartments of Yedicam and Burnuk Forest Directorates, #234 and #116, within Boyabat Forest Enterprise of Kastamonu Regional Directorate of Forestry.Material and Method: Two damaged and two control compartments from each directorate were compared to find if any site or stand parameters are related to the snow damage occurred in the area during 2014-2015 winter. Statistical analysis were carried out to test the significance of the parameters.Main results: Age, relative density, the number of trees, basal area and stand volume of the stand parameters, and slope, site class, terrain roughness and soil depth of the site parameters were statistically linked to the snow damage.Research highlights: The climate is a force to be reckoned with. New strategies must be devised and put into action in forest management. Studies have shown the mishaps. The lessons should be taken from the past experiences. Apparent mistakes must gradually be eliminated.

Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data

Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by disturbances are critical for estimating extent of damage, assessing economical influence and guiding forest management activities. In this study we focus on snow load damage detection from C-Band SAR images. Snow damage is one of the least studied forest damages, which is getting more common due to current climate trends. The study site was located in the southern part of Northern Finland and the SAR data were represented by the time series of C-band SAR scenes acquired by the Sentinel-1 sensor. Methods used in the study included improved k nearest neighbour method, logistic regression analysis and support vector machine classification. Snow damage recordings from a large snow damage event that took place in Finland during late 2018 were used as reference data. Our results showed an overall detection accuracy of 90%, indicating potential of C-band SAR for operational use in snow damage mapping. Additionally, potential of multitemporal Sentinel-1 data in estimating growing stock volume in damaged forest areas were carried out, with obtained results indicating strong potential for estimating the overall volume of timber within the affected areas. The results and research questions for further studies are discussed.

Insulator flashover caused by salt- contaminated snowstorms—lessons from the snow damage in Japan

Although flashovers on snow-capped insulators have been discussed since at least the 1950s, one of the advanced topics recently is flashovers following harsh contaminated snowstorms.

Measurement of Snow Density According to Regional Snow Depth in South Korea

As a result of abnormal weather conditions, such as climate change, the amount of snow damage has increased rapidly; hence, the need for countermeasures against heavy snow damage has increased. The most severe snow damage in South Korea has occurred in greenhouses. The snow damage caused in greenhouses is 76.7 billion KRW per year, and the cumulative damage area is 20,279 hectares. Snow density varies depending on snow depth, air temperature, and humidity. Depending on the weather conditions, snow density varies up to 7 times. Therefore, in this study, snow density was measured using snow depth field data. Based on weather data from the Korea Meteorological Administration and a disaster report, an area in Gangwon Province was selected as the target area. In Gangwon Province, five administrative districts were selected for field data sampling. The results showed that the density of snow increased as snow depth increased. Based on the results of this study, it was judged that the design of buildings and greenhouses could be more appropriate if snow load or snow density are used as variables. Keywords: Snow Damage, Snow Load, Sampling, Snow Density

Initiatives Targeting Snow Damage to Communication Facilities

Initiatives Targeting Snow Damage to Communication Facilities

地域素材「雪」をテーマにした教材開発とICT活用:～「雪害」を防ぐために～

地域素材「雪」をテーマにした教材開発とICT活用:～「雪害」を防ぐために～

“Uradome” treatment for prevention of snow damage and terrain parameters of moso bamboo (Phyllostachys pubescens) forest in Tsuruoka, Yamagata Prefecture

ABSTRACTMoso bamboo (Phyllostachys pubescens) is cultivated for edible bamboo shoot production in Japan. Yamagata Prefecture is located in a heavy-snowfall region at the northern limit of the cultivated area. However, snow damage to bamboo culms occurs every year and decreases bamboo shoot production. “Uradome” is a treatment that involves cutting off the upper portion of the new culms when the bamboo shoot is almost fully grown. We studied the effect of “Uradome” implementation on incidence of snow damage to bamboo culms. In addition, we investigated terrain characteristics of bamboo forest at sites where snow damage occurred. As a result, the higher the degree of “Uradome” was, the lower the damage rate was. Furthermore, the results revealed that implementation of “Uradome” prevents snow damage to culms. It is considered that snow damage may be prevented by rendering the culm shape straight as a result of “Uradome”. The results also indicated that snow damage is more likely to occur on steep slopes. Factors that influence the incidence and severity of snow damage are considered to differ between Japan and other countries. Therefore, it is desirable to adopt preventative measures against snow damage, such as the “Uradome” method, irrespective of the local climate and terrain.

Snow-stopper Pole as a Countermeasure for Preventing Snow Damage to Utility Poles

Snow-stopper Pole as a Countermeasure for Preventing Snow Damage to Utility Poles

Canopy openness and topographic habitat drive tree seedling recruitment after snow damage in an old-growth subtropical forest

We investigated the relative and combined effect of topography and light environment on the recruitment of seedlings in a subtropical forest after snow damage to the canopy. The tree seedling community in an old-growth subtropical forest was monitored using 500 2 m × 2 m seedling plots at six-month intervals for 2 years. With a focus on recruitment following canopy damage, we related abiotic and biotic environmental variables to seedling dynamics, and we tested if significant topographic and light habitat associations were present for seedlings recruiting via a torus translation test. Then, we used variance partitioning to examine the relative effects of spatial, topographic and light variables on the temporal assemblages of seedlings. A total of 3047 seedlings from 58 species recruited in the first 2 years following snow damage. At the community level, increases in seedling abundance and richness were positively correlated with canopy openness and negatively correlated with elevation. At the species level, both pioneer and late-successional tree species had more recruits in high light environment than in low light environment. 84.3% of the recruiting seedlings were significantly associated to either light environment (35.7%), topography (26.5%) or both (22.1%). Despite this, at the plot level, spatial variables (PCNM) explained the majority of the variability in seedling composition over time. Our results suggest that snow damage to the canopy increases species richness and abundance via light-facilitated seedling recruitment, and that the composition of recruiting seedlings was largely spatially dependent. Topographic habitat filtering acts as a persistent force in determining the recruitment of seedlings and increases in strength with increased light-facilitated seedling recruitment. Our results highlight that, in this subtropical forest, both light requirement and topographic specialization interact over time to play a key role in promoting coexistence of tree species through selection of individuals at the seedling stage. We also suggest exploring the possibilities of management intervention to speed up the recovery of this forest.

Snow damages on trees of an uneven age in mixed broadleaf forests: effects of topographical conditions and tree characteristics

Snow damage on natural stands is an important problem concerning mountainous forest management. In the present research, the frequency and intensity of heavy snow damage on natural stands and the relationship of damages with characteristics of trees, stand and topography were studied in mountainous forests of northern Iran. A systematic sampling design was applied to the study area (140 ha), with 122 circular plots. The grid dimension was 100 m and each plot area was 1000 m2. The four types of snow damage to trees include: crown damage (8.6%), stem breakage (5.4%), uprooting (3.2%), and bending (1.4%). The frequency of crown damage grew with an increase in the diameter at breast height (DBH), while the frequency of stem breakage, uprooting and bending decreased with an increase the DBH. The frequency of crown damage, stem breakage, and total damages was related to tree species (p < 0.01). Not all tree species were equally susceptible to snow damage. The amount of damage grew with increasing elevation and slope angle and decreased with increasing soil depth. Young trees (DBH < 25 cm) were more susceptible to snow damage than other trees. Snow damage decreased by as the trees in the stand became more mixed in age. The susceptibility of trees to snow damage increased by height and slenderness coefficient. With adequate silvicultural operation, snow damage can be reduced to a minimum level in these natural forests.

A STUDY ON REAL-TIME FLOOD MONITORING SYSTEM BASED ON SENSORS USING FLOOD DAMAGE INSURANCE MAP

Abstract. Typical types of natural disasters that occur in Korea are damages from heavy rain, storm, and heavy snow. In order to prepare for this, the storm and flood damage insurance program is operated. For this purpose, the risk of these damages is calculated for each region, and the storm and flood damage insurance map is created based on the risk. This map can provide insight into the degree of risk to wind and flood, snow damage, as well as policies to prevent and prepare for each type of natural disaster. In order to support decision-making by utilizing this insurance map, it is necessary to use with Storm and Flood Damage Information contents. In order to efficiently construct such disaster information contents, it is possible to utilize public data produced by various organizations. Korea has a public data portal to open various administrative information. The public data portal currently publishes and updates about 25,000 data from 700 organizations. In this study, the linkage system is designed that can construct disaster information contents by collecting public data and processing it so that it can be overlapped with the insurance map. The system automatically links public data to keep up-to-date disaster information content. It is expected that it will be able to prevent and prepare for natural disaster by supporting the decision making of decision makers related to flood damage.

A STUDY ON THE CONSTRUCTION OF DISASTER INFORMATION CONTENTS FOR THE STORM AND FLOOD DAMAGE INSURANCE MAP

Abstract. Typical types of natural disasters that occur in Korea are damages from heavy rain, storm, and heavy snow. In order to prepare for this, the storm and flood damage insurance program is operated. For this purpose, the risk of these damages is calculated for each region, and the storm and flood damage insurance map is created based on the risk. This map can provide insight into the degree of risk to wind and flood, snow damage, as well as policies to prevent and prepare for each type of natural disaster. In order to support decision-making by utilizing this insurance map, it is necessary to use with disaster Information contents. In order to efficiently construct such disaster information contents, it is possible to utilize public data produced by various organizations. Korea has a public data portal to open various administrative information. The public data portal currently publishes and updates about 25,000 data from 700 organizations. In this study, the linkage system is designed that can construct disaster information contents by collecting public data and processing it so that it can be overlapped with the insurance map. The system automatically links public data to keep up-to-date disaster information content. It is expected that it will be able to prevent and prepare for natural disaster by supporting the decision making of decision makers related to flood damage.

Multiple Regression Models of Snow Damage Prediction According to the Snow Damage Vulnerability Groups

The frequency of natural disaster has been increased due to the climate change, and the heavy snow has been also increased during the winter. Therefore, many researches have been conducted to estimate snow damage vulnerability. In this study, multiple regression models for snow damage prediction were developed using snow damage vulnerability which was calculated in the previous research. The snow damage vulnerability were categrized into 5 groups. The developed models were even applied in the area where never had a snow damage in the history. Three multiple regression models were developed according to the snow damage vulnerabilities. The input data for the model were snow vulnerability index, snow depth, the exceedance percentage of snow design criteria, daily relative humidity, daily minimum temperature, and daily maximum temperature. As a result, normalized root mean square error(NRMSE) was low enough to apply the models to estimate the snow damage. The developed models could be applied to estimate the snow damage even in the areas where the snow damage have not been occurred. Keywords: Snow Damage, Snow Damage Estimation, Multiple Regression Analysis, Snow Damage Vulnerability Groups

Snow Damage Vulnerability Analysis for Evaluation of Snow Damage in South Korea

Snow Damage Vulnerability Analysis for Evaluation of Snow Damage in South Korea