• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.31-44
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• 2019

The main objective of this research is to develop a hydroclimate drought index (HCDI) using the gridded climate data inputs in a Variable Infiltration Capacity (VIC) modeling platform. Typical drought indices, including, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Self-calibrated Palmer Drought Severity Index (SC-PDSI) in South Korea are also used and compared. Inverse Distance Weighting (IDW) method is applied to create the gridded climate data from 56 ground weather stations using topographic information between weather stations and the respective grid cell ($12km{\times}12km$). R statistical software packages are used to visualize HCDI in Google Earth. Skill score (SS) are computed to evaluate the drought predictability based on water information derived from the observed reservoir storage and the ground weather stations. The study indicates that the proposed HCDI with the gridded climate data input is promising in the sense that it can help us to predict potential drought extents and to mitigate its impacts in a changing climate. The longer term drought prediction (e.g., 9 and 12 month) capability, in particular, shows higher SS so that it can be used for climate-driven future droughts.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.311-321
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• 2017

Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.11
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• pp.1900-1911
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• 2010

This study focuses on clothing as one of the most seasonal products and investigates consumer behavior related to climate change adaptation. This study addressed four objectives: (1) to identify the clothing behavior of consumers for the adaptation to climate change; (2) to identify the effects of fashion involvement and climate sensitivity on clothing attitude for the adaptation to climate change; (3) to identify the effect of clothing purchase time on climate sensitivity and clothing attitude for the adaptation to climate change; and (4) to identify the effect of consumer demographics on climate sensitivity and clothing attitude for the adaptation to climate change. A survey questionnaire was developed and implemented to collect data for measuring clothing involvement, fashion involvement, and climate sensitivity. In addition, clothing involvement, clothing assortment needs, and clothing worn for the adaptation to climate change were measured. A total of 349 responses were analyzed by t-test, ANOVA and path analysis with SPSS18.0. The results of the analysis are as follows. Changes in temperature were considered more important than changes in weather for the functional needs of clothing, purchase needs, and assortment items needs. The assortment items wearing for the adaptation to climate change varied depending on the temperature and weather. Fashion involvement directly influenced clothing assortment needs and indirectly influenced the clothing worn for the adaptation to climate change. In terms of clothing purchase time, those purchasing clothing before the season begins, tended to have a high fashion involvement and clothing attitude for the adaptation to climate change. Those in their twenties and single, tended to be more sensitive to climate change. This study also discusses the implications for merchandising strategies.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.6
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• pp.615-631
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• 2012

This study provides an evaluation for capability of Integrated Climate and Air quality Modeling System (ICAMS) on future regional scale climate projection. Temperature and precipitation are compared between ground-level observation data and results of regional models (MM5) for the past 30 years over the Korean peninsula. The ICAMS successfully simulates the local-scale spatial/seasonal variation of the temperature and precipitation. The probability distribution of simulated daily mean and minimum temperature agree well with the observed patterns and trends, although mean temperature shows a little cold bias about $1^{\circ}C$ compared to observations. It seems that a systematic cold bias is mostly due to an underestimation of maximum temperature. In the case of precipitation, the rainfall in winter and light rainfall are remarkably simulated well, but summer precipitation is underestimated in the heavy rainfall phenomena of exceeding 20 mm/day. The ICAMS shows a tendency to overestimate the number of washout days about 7%. Those results of this study indicate that the performance of ICAMS is reasonable regarding to air quality predication over the Korean peninsula.

• Journal of Climate Change Research
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• v.2 no.2
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• pp.133-141
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• 2011

This study is to identify the trend of climate change in Gangwon-do by examining accumulated climate data such as temperature and precipitation in Gangneung city over the past about 100 years. The annual mean temperature and precipitation in Gangneung have increased by $1.4^{\circ}C$ and 14.7%, respectively, over the last 98 years (1912~2009). The trends of Gangneung showed that precipitation has intensified as the number of precipitation days decreased while its amount increased during the period. Based on the temperature data, spring and summer began earlier whereas the onsets of fall and winter were delayed. Summer has become longer and winter shorter by about a month. Averaging observation data from seven weather stations in Gangwon-do, the annual mean temperature and precipitation have increased by $0.8^{\circ}C$ and 21.0% respectively over the last 37 years (1973~2009). By region, Wonju city recorded the biggest increase of $1.6^{\circ}C$ in the annual mean temperature while Sokcho city the smallest increase of $0.4^{\circ}C$. In the annual mean precipitation, Daegwallweong recorded the biggest change of 22.2% and Wonju city the smallest of 12.0%.

• Journal of Environmental Impact Assessment
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• v.18 no.6
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• pp.347-357
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• 2009

This study conducted a vulnerability assessment on Korea's physical infrastructure to provide base data for developing strategies to strengthen Korea's ability to adapt to climate change. The assessment was conducted by surveying professionals in the field of infrastructure and climate change science. A vulnerability assessment was carried out for seven climate change events: average temperature increases, sea level rise, typhoons and storm surges, floods and heavy rain, drought, severe cold, and heat waves. The survey asked respondents questions with respect to the consequences of each climate change event, the urgency of adaptation to climate change, and the scale of investment for adaptation to each climate change event. Thereafter, management priorities for infrastructure were devised and implications for policy development were suggested. The results showed that respondents expected the possibility of "typhoons and storm surges" and "floods and heavy rain" to be the most high. Respondents indicated that infrastructure related to water, transportation, and the built environment were more vulnerable to climate change. The most vulnerable facilities included river related facilities such as dams and riverbanks in the "water" category and seaports and roads in the "transport and communication" category. The results found were consistent with the history of natural disasters in Korea.

• Journal of Environmental Health Sciences
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• v.45 no.3
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• pp.267-272
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• 2019

Objectives: The purpose of this study was to examine effect of anxiety about climate change on life satisfaction and mediating effect of subjective health status between anxiety about climate change and life satisfaction among older persons. Methods: This study used data from Statistics Korea 2018 Social Survey and a total of 7,870 older persons aged 65 and over were selected for the analyses. Descriptive statistics was used to identify characteristics of study participants and correlation analysis was used to examine the associations among anxiety about climate change, subjective health status, and life satisfaction. Also, multiple regression analyses were performed to examine effect of anxiety about climate change on life satisfaction and mediating effect of subjective health status between anxiety about climate change and life satisfaction. Results: Study findings show that anxiety about climate change had significant effect on life satisfaction. A higher level of anxiety decreased the level of life satisfaction of the elderly. A higher level of anxiety about climate change also decreased the level of subjective health status. In addition, the effect of anxiety about climate change on life satisfaction was partially mediated by subjective health status. Conclusions: Findings of the study suggest that the needs of older population should be considered in designing policy and interventions on climate change.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1286-1295
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• 2020

It has been increasingly difficult to predict the amounts of Acer mono sap to be collected due to droughts and cold waves caused by recent climate changes with few studies conducted on the prediction of its collection volume. This study thus set out to propose a Big Data prediction system based on meteorological information for the collection of Acer mono sap. The proposed system would analyze collected data and provide managers with a statistical chart of prediction values regarding climate factors to affect the amounts of Acer mono sap to be collected, thus enabling efficient work. It was designed based on Hadoop for data collection, treatment and analysis. The study also analyzed and proposed an optimal prediction model for climate conditions to influence the volume of Acer mono sap to be collected by applying a multiple regression analysis model based on Hadoop and Mahout.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.977-985
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• 2012

Data quality and climate forecasting performance deteriorates because of long climate data contaminated by non-climatic factors such as the station relocation or new instrument replacement. For a trusted climate forecast, it is necessary to implement data quality control and test inhomogeneous data. Before the inhomogeneity test, a reference series was created by $d$ index to measure the temperature series relationship between the candidate and surrounding stations. In this study, a inhomogeneity test to each season and climatological station was performed on the daily mean temperatures, daily minimum temperatures and daily maximum temperatures. After comparing two inhomogeneity tests, the traditional and the adjusted SNHT method, we found the adjusted SNHT method was slightly superior to the traditional one.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.433-446
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• 2021

In this study, the future flood inundation changes under a climate change were simulated in the Tonle Sap basin in Cambodia, one of the countries with high vulnerability to climate change. For the flood inundation simulation using the rainfall-runoff-inundation (RRI) model, globally available geological data (digital elevation model [DEM]; hydrological data and maps based on Shuttle elevation derivatives [HydroSHED]; land cover: Global land cover facility-moderate resolution imaging spectroradiometer [GLCF-MODIS]), rainfall data (Asian precipitation-highly-resolved observational data integration towards evaluation [APHRODITE]), climate change scenario (HadGEM3-RA), and observational water level (Kratie, Koh Khel, Neak Luong st.) were constructed. The future runoff from the Kratie station, the upper boundary condition of the RRI model, was constructed to be predicted using the long short-term memory (LSTM) model. Based on the results predicted by the LSTM model, a total of 4 cases were selected (representative concentration pathway [RCP] 4.5: 2035, 2075; RCP 8.5: 2051, 2072) with the largest annual average runoff by period and scenario. The results of the analysis of the future flood inundation in the Tonle Sap basin were compared with the results of previous studies. Unlike in the past, when the change in the depth of inundation changed to a range of about 1 to 10 meters during the 1997 - 2005 period, it occurred in a range of about 5 to 9 meters during the future period. The results show that in the future RCP 4.5 and 8.5 scenarios, the variability of discharge is reduced compared to the past and that climate change could change the runoff patterns of the Tonle Sap basin.