• Korean Journal of Soil Science and Fertilizer
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• v.47 no.2
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• pp.71-79
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• 2014

Climate change has been social and environmental issues, it typically indicates the trend changes of not only temperature but also rainfall. There is a need to consider climate changes in a long-term soil erosion estimation since soil loss in a watershed can be varied by the changes of rainfall intensity and frequency of torrential rainfall. The impacts of rainfall trend changes on soil loss, one of climate changes, were estimated using Sediment Assessment Tool for Effective Erosion Control (SATEEC) employing L module with current climate scenario and future climate scenario collected from the Korea Meteorological Administration. A 62 $km^2$ watershed was selected to explore the climate changes on soil loss. SATEEC provided an increasing trend of soil loss with the climate change scenarios, which were 182 ton/ha/year in 2010s, 169 ton/ha/year in 2020s, 192 ton/ha/year in 2030s,182 ton/ha/year in 2040s, and 218 ton/ha/year in 2050s. Moreover, it was found that approximately 90% of agricultural area in the watershed displayed the soil loss of 50 ton/ha/year which is exceeding the allow able soil loss regulation by the Ministry of Environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.120-139
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• 2020

This study is to assess the future agricultural land use and climate change impacts on irrigation water requirement using CLUE-s(Conversion of Land Use and its Effects at Small regional extent) and RCP(Representative Concentration Pathway) 4.5 and 8.5 HadGEM3-RA(Hadley Centre Global Environmental Model version 3 Regional Atmosphere) scenario. For Nonsan city(55,517.9ha), the rice paddy, upland crop, and greenhouse cultivation were considered for agricultural land uses and DIROM(Daily Irrigation Reservoir Operation Model) was applied to benefited areas of Tapjeong reservoir (5,713.3ha) for Irrigation Water Requirement(IWR) estimation. For future land use change simulation, the CLUE-s used land uses of 2007, 2013, and 2019 from Ministry of Environment(MOE) and 6 classes(water, urban, rice paddy, upland crop, forest, and greenhouse cultivation). In 2100, the rice paddy and upland crop areas decreased 5.0% and 7.6%, and greenhouse cultivation area increased 24.7% compared to 2013. For the future climate change scenario considering agricultural land use change, the RCP 4.5 and RCP 8.5 2090s(2090~2099) IWR decreased 2.1% and 1.0% for rice paddy and upland crops, and increased 11.4% for greenhouse cultivation compared to pure application of future climate change scenario.

• Journal of Climate Change Research
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• v.5 no.4
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• pp.323-329
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• 2014

Since the terrain of Korea is complex, micro- as well as meso-climate variability is extreme by locations in Korea. In particular, air temperature of agricultural fields is influenced by topographic features of the surroundings making accurate interpolation of regional meteorological data from point-measured data. This study was carried out to compare spatial interpolation methods to estimate air temperature in agricultural fields surrounded by rugged terrains in South Korea. Four spatial interpolation methods including Inverse Distance Weighting (IDW), Spline, Ordinary Kriging (with the temperature lapse rate) and Cokriging were tested to estimate monthly air temperature of unobserved stations. Monthly measured data sets (minimum and maximum air temperature) from 588 automatic weather system(AWS) locations in South Korea were used to generate the gridded air temperature surface. As the result, temperature lapse rate improved accuracy of all of interpolation methods, especially, spline showed the lowest RMSE of spatial interpolation methods in both maximum and minimum air temperature estimation.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1143-1152
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• 2009

A canonical correlation analysis(CCA)-based method is proposed for prediction of future climate change which combines information from ensembles of atmosphere-ocean general circulation models(AOGCMs) and observed climate values. This paper focuses on predictions of future climate on a regional scale which are of potential economic values. The proposed method is obtained by coupling the classical CCA with empirical orthogonal functions(EOF) for dimension reduction. Furthermore, we generate a distribution of climate responses, so that extreme events as well as a general feature such as long tails and unimodality can be revealed through the distribution. Results from real data examples demonstrate the promising empirical properties of the proposed approaches.

• Journal of Wetlands Research
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• v.17 no.1
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• pp.80-90
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• 2015

Variability of precipitation pattern and intensity are increasing due to the urbanization and industrialization which induce increasing impervious area and the climate change. Therefore, more severe urban inundation and flood damage will be occurred by localized heavy precipitation event in the future. In this study, we analyze the future frequency based precipitation under climate change based on the regional frequency analysis. The observed precipitation data from 58 stations provided by Korea Meteorological Administration(KMA) are collected and the data period is more than 30 years. Then the frequency based precipitation for the observed data by regional frequency analysis are estimated. In order to remove the bias from the simulated precipitation by RCP scenarios, the quantile mapping method and outlier test are used. The regional frequency analysis using L-moment method(Hosking and Wallis, 1997) is performed and the future frequency based precipitation for 80, 100, and 200 years of return period are estimated. As a result, future frequency based precipitation in South Korea will be increased by 25 to 27 percent. Especially the result for Jeju Island shows that the increasing rate will be higher than other areas. Severe heavy precipitation could be more and more frequently occurred in the future due to the climate change and the runoff characteristics will be also changed by urbanization, industrialization, and climate change. Therefore, we need prepare flood prevention measures for our flood safety in the future.

• Journal of Korean Society of Rural Planning
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• v.26 no.4
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• pp.81-91
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• 2020

This study was conducted to analyse climate change impact on agriculture infrastructure and propose improved measures on vulnerable areas. Recently, Climate change has resulted in damaging effects on agricultural fields through increases in drought intensity and flood risk. It is expected that this impact will increase over time. This study shows that Gyeong-gi and Chung-nam provinces are affected by drought and Gyeong-buk and Gyeong-nam provinces are affected by heavy rain. However, there are also regional variations within each province. Agricultural infrastructure affected by drought may also be affected by heavy rain. Increased damages on the infrastructure due to increased extreme weather events require preventive measures especially in vulnerable areas. In order to minimize the damage by climate change, we need to introduce a reform in the system which selects project region by analysing climate change impacts. Furthermore, impact assessment of climate change from projects such as 'water supply diversification', 'flooded farmland improvement', and 'irrigation facility reinforcement' also need to be adopted to improve the measures. The results of this study are expected to provide a foundation for establishing measures on coping with climate change in the agricultural sector.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.4
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• pp.465-482
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• 2024

The global use of groundwater in coastal areas has increased. Events such as seawater intrusion (SWI) are expected to increase along with the acceleration of natural disasters owing to environmental changes such as climate change, resulting in large-scale damage worldwide. Current trends in the research of coastal groundwater and related natural disasters include testing and verifying technologies using major case studies from individual countries. We identified global research trends in coastal groundwater, related these trends to changing environments and climate, and confirmed the qualitative and quantitative growth of these studies. This study describes the theoretical background and techniques for coastal groundwater analysis and details regional-scale SWI indicators based on analytical and numerical studies. This review highlights recent technologies that consider uncertainty and promotes discussions on field data obtained using new technologies. Finally, the research findings and trends for a regional coastal aquifer in Korea are discussed to describe recent SWI approaches for groundwater resources.

• Asian Journal of Atmospheric Environment
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• v.11 no.4
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• pp.330-343
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• 2017

Climate change is an important issue, with many researches examining not only future climatic conditions, but also the interaction of climate and air quality. In this study, a new version of the emissions processing software tool - Python-based PRocessing Operator for Climate and Emission Scenarios (PROCES) - was developed to support climate and atmospheric chemistry modeling studies. PROCES was designed to cover global and regional scale modeling domains, which correspond to GEOS-Chem and CMAQ/CAMx models, respectively. This tool comprises of one main system and two units of external software. One of the external software units for this processing system was developed using the GIS commercial program, which was used to create spatial allocation profiles as an auxiliary database. The SMOKE-Asia emissions modeling system was linked to the main system as an external software, to create model-ready emissions for regional scale air quality modeling. The main system was coded in Python version 2.7, which includes several functions allowing general emissions processing steps, such as emissions interpolation, spatial allocation and chemical speciation, to create model-ready emissions and auxiliary inputs of SMOKE-Asia, as well as user-friendly functions related to emissions analysis, such as verification and visualization. Due to its flexible software architecture, PROCES can be applied to any pregridded emission data, as well as regional inventories. The application results of our new tool for global and regional (East Asia) scale modeling domain under RCP scenario for the years 1995-2006, 2015-2025, and 2040-2055 was quantitatively in good agreement with the reference data of RCPs.

• Atmosphere
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• v.17 no.2
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• pp.171-183
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• 2007

Trends of daily maximum and minimum temperatures in major cities of South Korea (Seoul, Busan, Incheon, Daegu, and Ulsan) during the past 40 years (1961-2000) were investigated. Temperature records for the Chupungryeong station were compared with those of the large cities because of the rural environment of the station. There were distinct warming trends at all stations, although the warming rates depend on each station's local climate and environment. The warming rates in Korea are much greater than the global warming trends, by a factor of 3 to 4. The most increasing rate in daily maximum temperature was at Busan with $0.43^{\circ}C$ per decade, the most increasing rate in daily minimum temperature was at Daegu with $0.44^{\circ}C$ per decade. In general, the warming trends of the cities were most pronounced in winter season with an increasing rate of $0.5^{\circ}C$/decade at least. Diurnal temperature range shows positive or negative trends according to the regional climate and environmental change. The frequency distribution of the daily temperatures for the past 40 years at Seoul and Chupungryeong shows that there have been reductions in cold day frequencies at both stations. The results imply that the impacts on human health might be positive in winter and adverse in summer if the regional warming scenario by the current regional climate model reflects future climate change in Korea.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.107-114
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• 1999

The large-scale distribution of crops Is usually determined by climate. We present the results of a climate-crop prediction based on spatial bio-physical process model approach, implemented in a GIS (Geographic Information System) environment using several regional and global agriculture-environmental databases. The model utilizes daily climate data like temperature, rainfall, solar radiation being generated stocastically by in-built model weather generator to determine the daily biomass and finally the crop yield. Crops are characterized by their specific growing period requirements, photosynthesis, respiration properties and harvesting index properties. Temperature and radiation during the growing period controls the development of each crop. The model simulates geographic/spatial distribution of climate by which a crop-growing belt can also be determined. The model takes both irrigated and non-irrigated area crop productivity into account and the potential increase in productivity by the technical means like mechanization is not considered. All the management input given at the base year 1995 was kept same for the next twenty-year changes until 2015. The simulated distributions of crops under current climatic conditions coincide largely with the current agricultural or specific crop growing regions. Simulation with assumed weather generated derived climate change scenario illustrate changes in the agricultural potential. There are large regional differences in the response across the country. The north-south and east-west regions responded differently with projected climate changes with increased and decreased productivity depending upon the crops and scenarios separately. When water was limiting or facilitating as non-irrigated and irrigated area crop-production effects of temperature rise and higher $CO_2$ levels were different depending on the crops and accordingly their production. Rise in temperature led to yield reduction in case of maize and rice whereas a gain was observed for wheat crop, doubled $CO_2$ concentration enhanced yield for all crops and their several combinations behaved differently with increase or decrease in yields. Finally, with this spatial modeling approach we succeeded in quantifying the crop productivity which may bring regional disparities under the different climatic scenarios where one region may become better off and the other may go worse off.