• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.4
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• pp.248-253
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• 2002

This study was to provide information about snow falling phenomenon in Korea for 1934 years from BC 6 to 1928 based on the records of old literatures, which are the true record of the Chosun dynasty, records of king Kojong and Soonjong, and some data including history of the Koryo in internet home page of Korea meteorological administration. Key words used in search procedure were totally 20 words such as snow, heavy snow, big snow, snow pellets, snowstorm, avalanche, etc. The searching contents consisted of the time of the first and the last snow, the amount of snow falling, snow damage, the thought about heavy snow phenomenon, and unusual weather conditions related to snow. The earliest record for the first snow was July of the rural calendar in 733 and the latest record for the last snow was June 11 of the lunar calendar. From these records, it could be estimated that there were some snow falling even in summer season. The amount of almost heavy snow ranged from 1.2 m to 1.5 m, but sometimes there were some records about the amount of snow falling higher than 3 meters. It was also found that there were three records about big heavy snow damages. In 1524 and 1525, approximately 100 and 140 peoples in Kyungsung, Hamgyung Province were dead due to heavy snowstorm. It was also recorded that 91 people in Jeiu island were dead in 1670 because of snow damage. Some singular records about snow were also found in old literatures. There was a congratulatory ceremony of new snow when the first snow was falling in the year. There was also a ritual praying for snow when there was no snow in the year. It was also found that there was snow falling with worms and red snow falling.

• Journal of Korean Society of Forest Science
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• v.73 no.1
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• pp.63-69
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• 1986

In general, the snow injury in forestry is an unusual disaster. The degree of snow injury varies greatly depending on stand density and the local topography. This study was conducted to investigate the snow injury in analyzing the demaged by snow-fall in Jinju, Gyeongsangnamdo. The results obtained were summarized as follow; Among 466 total damaged trees, 425 trees were broken and 41 trees were uprooted, the ratio of damage were 5.22%, 2.49%, 0.92% and 0.2% for Pinus densiflora, Pinus thunbergii, Pinus rigida, Alnus hirsuta respectively. The 95% of the damage trees were in the range of 3 to 11 m for height and in the range of 3 to 20 cm for D. B. H.. The directions of the damage trees had a large influence by direction of the wind, but they shown at high tendency to aspect of the slope relatively. The 82% of the damaged trees ranged from 11 to 24 age. The ratio of broken height ($H_B/H$) indicated that the damage was most frequent in the part of stem as 24%, 45%, 31% in the part of the root collar (0.1), stem (0.2-0.4), crown (0.5-1.0) respectively. In general, trees with stem-form coefficient ($H_B/D$) over 0.7-0.8 are apt to suffer by snow damage. The average of stem-form coefficient of trees in this area was 1.06. Therefore, the ratio of damage was high tendency as 3.14%. These results indicate that it is necessary to apply pertinent tending which will increase in resistance of snow damage. As avalanches from the flank of soil erosion rise in an importance matter in present, it should also be considered to measures for prevention and restoration.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.145-145
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• 2022

Availability of abundant water resources data in developing countries is a great concern that has hindered the adoption of deep learning techniques (DL) for disaster prevention and mitigation. On the contrary, over the last two decades, a sizeable amount of DL publication in disaster management emanated from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster management in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 2003 - 2022 using Web of Science, Scopus, VOSviewer software and PRISMA model. Results show that four major disasters - pluvial / fluvial flooding, land subsidence, drought and snow avalanche are the most prevalent. Also, recurrent flash floods and landslides caused by irregular rainfall pattern, abundant freshwater and mountainous terrains made India the only developing country with an impressive DL adoption rate of 50% publication count, thereby setting the pace for other developing countries. Further analysis indicates that economically-disadvantaged countries will experience a delay in DL implementation based on their Human Development Index (HDI) because DL implementation is capital-intensive. COVID-19 among other factors is identified as a driver of DL. Although, the Long Short Term Model (LSTM) model is the most frequently used, but optimal model performance is not limited to a certain model. Each DL model performs based on defined modelling objectives. Furthermore, effect of input data size shows no clear relationship with model performance while final model deployment in solving disaster problems in real-life scenarios is lacking. Therefore, data augmentation and transfer learning are recommended to solve data management problems. Intensive research, training, innovation, deployment using cheap web-based servers, APIs and nature-based solutions are encouraged to enhance disaster preparedness.