• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 2005.10a
• /
• pp.156-163
• /
• 2005

This study was attempted to provide some fundamental data for safety structrural design of biological production facility. Wind load and snow load, acting on agricultural structures is working more sensitive than any other load. Therefore, wind speed and snow depth according to return periods for design load estimation were calculated by frequency analysis using the weather data(maximum instantaneous wind speed, maximum wind speed, maximum depth of snow cover and fall) of 68 regions in Korea. Equations for estimating maximum instantaneous wind speed with maximum wind speed were developed for all, inland and seaside regions. The results were about the same as the current eqution in general. Design wind speed and snow depth according to return periods were calculated and Local design wind load and snow load depending on return periods were presented together with iso-wind speed and iso-snow depth maps. The calculated design snow depth by maximum depth of snow cover were higher than design snow depth by maximum depth of snow fall. Considering wind speed and snow depth, protected cultivation is very difficult in Ullungdo, Gangwon seaside and contiguity inland regions, and strong structural design is needed in the west-south seaside against wind speed, and structure design of biological production facility in these regions need special consideration.

• Wind and Structures
• /
• v.37 no.1
• /
• pp.25-38
• /
• 2023

Due to the complexity and difficulty in meeting the multiphase flow complexity, similarity, and multiscale characteristics, the mechanism of snow drift is so complicated that the snow deposition prediction is still inaccurate and needs to be far improved. Meanwhile, the validation of prediction methods is also limited due to a lack of field-measured data about snow deposition. To this end, a field measurement activity about snow deposition around a cube with time was carried out, and the snow accumulation process was measured under blowing snow conditions in northwest China. The maximum snow depth, snow profile, and variation in snow depth around the cube were discussed and analyzed. The measured results indicated three stages of snow accumulation around the cube. First, snow is deposited in windward, lateral and leeward regions, and then the snow depth in windward and lateral regions increases. Secondly, when the snow in the windward region reaches its maximum, the downwash flow erodes the snow against the front wall. Meanwhile, snow range and depth in lateral regions have a significant increase. Thirdly, a narrow road in the leeward region is formed with the increase in snow range and depth, which results in higher wind speed and reforming snow deposition there. The field measurement study in this paper not only furthers understanding of the snow accumulation process instead of final deposition under complex conditions but also provides an important benchmark for validating prediction methods.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.22 no.2
• /
• pp.92-101
• /
• 2020

To prevent and mitigate damage to farms due to heavy snowfall, snow weight information should be provided in addition to snow depth. This study reviews four formulae regarding snow density and weight used in extant studies and applies them in Suwon area to estimate snow weight in Korea. We investigated the observed snow depth of 94 meteorological stations and automatic weather stations (AWS) data over the past 30 years (1988-2017). Based on the spatial distribution of snow depth by area in Korea, much of the fresh snow cover, due to heavy snowfall, occurred in Jeollabuk-do and Gangwon-do. Record snowfalls occurred in Gyeongsangbuk-do and Gangwon-do. However, the most recent heavy snowfall in winter occurred in Gyeonggi-do, Gyeongsangbuk-do, and Jeollanam-do. This implies that even if the snow depth is high, there is no significant damage unless the snow weight is high. The estimation of snow weight in Suwon area yielded different results based on the calculation method of snow density. In general, high snow depth is associated with heavy snow weight. However, maximum snow weight and maximum snow depth do not necessarily occur on the same day. The result of this study can be utilized to estimate the snow weight at other locations in Korea and to carry out snow weight prediction based on a numerical model. Snow weight information is expected to aid in establishing standards for greenhouse design and to reduce the economic losses incurred by farms.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.37 no.2
• /
• pp.325-331
• /
• 2017

Due to the climate change, natural disaster has been occurred more frequently and the number of snow disasters has been also increased. Therefore, many researches have been conducted to predict the amount of snow damages and to reduce snow damages. In this study, snow damages over last 21 years on the Natural Disaster Report were analyzed. As a result, Chungcheong-do, Jeolla-do, and Gangwon-do have the highest number of snow disasters. The multiple linear regression models were developed using the snow damage data of these three provinces. Daily fresh snow depth, daily maximum, minimum, and average temperatures, and relative humidity were considered as possible inputs for climate factors. Inputs for socio-economic factors were regional area, greenhouse area, farming population, and farming population over 60. Different regression models were developed based on the daily maximum snow depth. As results, the model efficiency considering all damage (including low snow depth) data was very low, however, the model only using the high snow depth (more than 25 cm) has more than 70% of fitness. It is because that, when the snow depth is high, the snow damage is mostly caused by the snow load itself. It is suggested that the 25 cm of snow depth could be used as the snow damage threshold based on this analysis.

• Journal of Korea Water Resources Association
• /
• v.50 no.10
• /
• pp.681-690
• /
• 2017

This study estimated the daily maximum snow depth using the Artificial Neural Network (ANN) model in Korean Peninsula. First, the optimal ANN model structure was determined through the trial-and-error approach. As a result, daily precipitation, daily mean temperature, and daily minimum temperature were chosen as the input data of the ANN. The number of hidden layer was set to 1 and the number of nodes in the hidden layer was set to 10. In case of using the observed value as the input data of the ANN model, the cross validation correlation coefficient was 0.87, which is higher than that of the case in which the daily maximum snow depth was spatially interpolated using the Ordinary Kriging method (0.40). In order to investigate the performance of the ANN model for estimating the daily maximum snow depth of the ungauged area, the input data of the ANN model was spatially interpolated using Ordinary Kriging. In this case, the correlation coefficient of 0.49 was obtained. The performance of the ANN model in mountainous areas above 200m above sea level was found to be somewhat lower than that in the rest of the study area. This result of this study implies that the ANN model can be used effectively for the accurate and immediate estimation of the maximum snow depth over the whole country.

• Korean Journal of Remote Sensing
• /
• v.29 no.3
• /
• pp.315-324
• /
• 2013

Spatial information of snow cover and depth distribution is a key component for snowmelt runoff modeling. Wide snow cover areas can be extracted from NOAA AVHRR or Terra MODIS satellite images. In this study eight sets of annual snow cover data (1997-2006) in two mountainous watersheds (A: Chungju-Dam and B: Soyanggang-Dam) were extracted using NOAA AVHRR images. The distribution of snow depth within the Snow Cover Area (SCA) was generated using snowfall data from ground meteorological observation stations. Snow depletion characteristics for the two watersheds were analyzed snow distribution time series data. The decreased pattern of SCA can be expressed as a logarithmic function; the determination coefficients were 0.62 and 0.68 for the A and B watersheds, respectively. The SCA decreased over 70% within 10 days from the time of maximum SCA.

• Journal of Korea Water Resources Association
• /
• v.52 no.12
• /
• pp.1001-1009
• /
• 2019

Due to recent increasing heavy snow in Korea, the damage caused by heavy snow is also increasing. In Korea, there are many efforts including establishing disaster prevention measures to reduce the damage throughout the country, but it is difficult to establish the design criteria due to the characteristics of heavy snow. In this study, snowfall frequency analysis was performed to estimate design snow depths using observed snow depth data at Jinju, Changwon and Hapcheon stations. The conventional frequency analysis is sometime limted to apply to the snow depth data containing zero values which produce unrealistc estimates of distributon parameters. To overcome this problem, this study employed mixed distributions based on Lognormal, Generalized Pareto (GP), Generalized Extreme Value (GEV), Gamma, Gumbel and Weibull distribution. The results show that the mixed distributions produced smaller design snow depths than single distributions, which indicated that the mixed distributions are applicable and practical to estimate design snow depths.

• Journal of Soil and Groundwater Environment
• /
• v.20 no.5
• /
• pp.16-25
• /
• 2015

The City of Boulder is located at an average elevation of 1,655 m (5,430 feet), the foothills of the Rocky Mountains in Colorado. Its daily air temperature is much varying and snow is very frequent and heavy even in spring. This paper examines characteristics of shallow (surface and depth = 10 cm) soil temperatures measured from January to May 2015 in the high elevation city Boulder, Colorado. The surface soil temperature quickly responded to the air temperature with the strongest periodicity of 1 day while the subsurface soil temperatures showed a less correlation and delayed response with that. The short-time Fourier of the soil temperatures uncovered their very low frequencies characteristics in heavy snow days while it revealed high frequencies of their variations in warm spring season. The daily minimum air temperature exhibited high cross-correlations with the soil temperatures without lags unlike the maximum air temperature, which is derived from its higher and longer auto-correlation and stronger spectrums of low frequencies than the maximum air temperature. The snow depth showed an inverse relationship with the soil temperature variations due to snow's low thermal conductivity and high albedo. Multiple regression for the soil temperatures using the air temperature and snow depth presented its predicting possibility of them even though the multiple r² of the regression is not that much satisfactory (r² = 0.35-0.64).

• Journal of Bio-Environment Control
• /
• v.1 no.1
• /
• pp.1-13
• /
• 1992

This study was attemped to provide some fundamental data for the safety structural design of biological production facility. Wind speed and snow depth according to recurrence intervals for design load estimation were calculated by frequency analysis using the weather data of 60 stations in Korea. The following results were obtained : 1. Type-I extremal distribution was selected for the probability density function of yearly maximum wind speed and snow depth and result of Chi-square goodness of fit showed highly significance at most regions. 2. Design frequency factors for given number of samples and recurrence intervals were calculated, and also design wind speed and snow depth as shown in Table 5-Table 6 and Fig.3-Fig.4 were derived. 3. About 46.4% of the winds having maximum wind speed at every station was analyzed to be same direction, and the consideration of this fact may improve the structural safety. 4. Considering wind speed and snow depth, protected cultivation is very difficult in Ullungdo and the Youngdong districts, and strong structural design is needed in the Chungnam and Junbuk west seaside against snow depth and the west-south seaside against wind speed in Korea.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.2B
• /
• pp.177-185
• /
• 2008

Accurate monitoring of snow cover is a key component for studying climate and global as well as for daily weather forecasting and snowmelt runoff modelling. The few observed data related to snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) were built for 7 major watersheds in South Korea. The decrease pattern of SCA for time (day) was expressed as exponentially decay function, and the determination coefficient was ranged from 0.46 to 0.88. The SCA decreased 70% to 100% from the maximum SCA when 10 days passed.