• Journal of Climate Change Research
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• v.7 no.3
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• pp.257-268
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• 2016

Both economic growth and industrial structure have great influence on energy consumption and GHG emissions. This study analyzed long-term scenarios for GHG emissions projections considering economic growth and industry value added change. In consideration of 3 GDP and 3 industry value added outlook, total 9 scenarios were set; 'Assembly Industry Baseline(AI)', 'Assembly KEIT industry(AK)', 'Assembly Advanced Country industry(AA)', 'KDI Industry Baseline(KI)', 'KDI KEIT industry(KK)', 'KDI Advanced Country industry(KA)', 'OECD Industry Baseline(OI)', 'OECD KEIT industry(OK)', and 'OECD Advanced Country industry(OA)' scenarios. In consideration of the GDP increase rate and industry value added outlook, it is estimated that AI scenario's GHG emissions would be 777 million tons of $CO_2eq$ in 2030. On the other hand, in the case of OA scenario, GHG emissions would be 560.2 million tons of $CO_2eq$ in 2030. Differences between AI scenario's and OA scenario's were 216.8 million tons of $CO_2eq$. It can be identified by that GDP and industry value added change have great influence on GHG emissions. In view of the fact that Korea's amount of GHG emission reduction targets in 2030 were 218.6 million tons of $CO_2eq$ that the result of this research could give us valuable insight.

• International Journal of Contents
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• v.17 no.1
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• pp.61-70
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• 2021

As the resolution of climate change scenario data applied with regional models increased, Earth System Grid Federation (ESGF) was established around major climate-related organizations to jointly operated and manage large-scale climate data. ESGF developed standard software to provide model output, observation data management, dissemination, and analysis using Peer to Peer (P2P) computing technology. Roles of each institution were divided into index and data nodes. Therefore, ESGF data node was established at APEC Climate Center in Korea on behalf of Asia to share data on climate change scenarios of CORDEX-East Asia (CORDEX-EA) to study climate changes in Eastern Asia. Climate researchers are expected to play a large role in researching causes of global warming and responding to climate change by providing CORDEX-EA regional model data to the world through ESGF data node.

• Journal of Korean Society of Forest Science
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• v.100 no.2
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• pp.256-265
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• 2011

This study was aiming at assessing the vulnerability of forest distribution by the A2 and B1 climate change scenarios of Intergovernmental Panel on Climate Change (IPCC). The vulnerability of forest distribution was assessed using its sensitivity and adaptation to climate change with the help of the simulations of Korean-specific forest distribution model, so-called the Thermal Analogy Group (TAG), and the Plant Functional Type (PFT) defined in the HyTAG (Hydrological and Thermal Analogy Groups) model. As a result, the vulnerable area occupied 30.78% and 2.81% of Korea in A2 and B1 scenario, respectively. When it comes to the administrative districts, Pusan in A2 and Daegu in B1 appeared the most vulnerable area. This study would be employed into preparation of adaptative measures for forest in future in terms of using climate change scenarios reflecting different future development conditions.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.53-62
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• 2013

Land-use/cover changes not only amplify or alleviate influence of climate changes but also they are representative factors to affect environmental change along with climate changes. Thus, the use of land-use/cover changes scenario, consistent climate change scenario is very important to evaluate reliable influences by climate change. The purpose for this study is to predict and analyze the future urban growth considering social and economic scenario from RCP scenario suggested by the 5th evaluation report of IPCC. This study sets land-use/cover changes scenario based on storyline from RCP 4.5 and 8.5 scenario. Urban growth rate for each scenario is calculated by urban area per person and GDP for the last 25 years and regression formula based on double logarithmic model. In addition, the urban demand is predicted by the future population and GDP suggested by the government. This predicted demand is spatially distributed by the urban growth probability map made by logistic regression. As a result, the accuracy of urban growth probability map is appeared to be 89.3~90.3% high and the prediction accuracy for RCP 4.5 showed higher value than that of RCP 8.5. Urban areas from 2020 to 2050 showed consistent growth while the rate of increasing urban areas for RCP 8.5 scenario showed higher value than that of RCP 4.5 scenario. Increase of urban areas is predicted by the fact that famlands are damaged. Especially RCP 8.5 scenario indicated more increase not only farmland but also forest than RCP 4.5 scenario. In addition, the decrease of farmland and forest showed higher level from metropolitan cities than province cities. The results of this study is believed to be used for basic data to clarify complex two-way effects quantitatively for future climate change, land-use/cover changes.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.189-189
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• 2016

To generate information that contributes to climate change risk management, it is important to perform a precise assessment on the impact in diverse aspects. Considering this academic necessity, Japanese government launched continuous research project for the climate change impact assessment, and one of the representative project is Program for Risk Information on Climate Change (Sousei Program), Theme D; Precise Impact Assessment on Climate Change (FY2012 ~ FY2016). In this research program, quantitative impact assessments have been doing from a variety of perspectives including natural hazards, water resources, and ecosystems and biodiversity. Especially for the natural hazards aspect, a comprehensive impact assessment has been carried out with the worst-case scenario of typhoons, which cause the most serious weather-related damage in Japan, concerning the frequency and scale of the typhoons as well as accompanying disasters by heavy rainfall, strong winds, high tides, high waves, and landslides. In this presentation, a framework of comprehensive impact assessment with the worst-case scenario under the climate change condition is introduced based on a case study of Theme D in Sousei program There are approx. 25 typhoons annually and around 10 of those approach or make landfall in Japan. The number of typhoons may not change increase in the future, but it is known that a small alteration in the path of a typhoon can have an extremely large impact on the amount of rain and wind Japan receives, and as a result, cause immense damage. Specifically, it is important to assess the impact of a complex disaster including precipitation, strong winds, river overflows, and high tide inundation, simulating how different the damage of Isewan Typhoon (T5915) in 1959 would have been if the typhoon had taken a different path, or how powerful or how much damage it would cause if Isewan Typhoon occurs again in the future when the sea surface water temperature has risen due to climate changes (Pseudo global warming experiment). The research group also predict and assess how the frequency of "100-years return period" disasters and worst-case damage will change in the coming century. As a final goal in this research activity, the natural disaster impact assessment will extend not only Japan but also major rivers in Southeast Asia, with a special focus on floods and inundations.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Journal of Climate Change Research
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• v.7 no.3
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• pp.341-349
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• 2016

This study is intended to set a greenhouse gas emission scenario based on green remodeling pilot project (Annex building of Seoul Customs Office) using LEAP model, a long-term energy plan analysis model, to calculate the energy saving and greenhouse gas emission till year 2035 as well as to analyze the effect of electric power saving cost. Total 4 scenarios were made, Baseline scenario, assuming the past trend is to be maintained in the future, green remodeling scenario, reflecting actual green remodeling project of Seoul Customs Office, behavior improvement and renewable energy supply, and Total scenario. According to the analysis result, the energy demand in 2035 of Baseline scenario was 6.1% decreased from base year 2013, that of green remodeling scenario was 17.5%, that of behavior improvement and renewable energy supply scenario was 21.1% and that of total scenario was 27.3%. The greenhouse emission of base year 2013 was $878.2tCO_2eq$, and it was expected $826.3tCO_2eq$, approx. 5.9% reduced, in 2035 by Baseline scenario. the cumulative greenhouse gas emission saving of the analyzing period were $-26.5tCO_2eq$ by green remodeling scenario, $2.8k\;tCO_2eq$ by behavior improvement and renewable energy supply scenario, and $2.0k\;tCO_2eq$ by total scenario. In addition the effect of electricity saving cost through energy saving has been estimated, and it was approx. 634 million won by green remodeling scenario and appro. 726 million won by behavior improvement and renewable energy supply scenario. So it is analyzed that of behavior improvement and renewable energy supply scenario would be approx. 12.7% higher than that of green remodeling scenario.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.101-112
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• 2015

This study was conducted to predict greenhouse gas (GHG) emission from a radish field by future climate change scenario. A radish field located at Chuncheon-si Gangwon-do was selected, and A1B Special Report on Emission Scenario (SRES) of the IPCC (Intergovernmental panel on climate change) was applied to simulate the future potential climate change. Rainfall and temperature data were predicted to be increased by 8.4 % and 1.9 % in 2040s, 35.9 % and 27.0 % in 2060s, 19.2 % and 30.8 % in 2090s, respectively, compared to the climate data in 2010s. The $N_2O$, $CO_2$, and $CH_4$ emission were estimated to be increased by 0.4 up to 2.4 kg/ha/yr, by 500.5 up to 734.5 kg/ha/year, and by 29.4 up to 160.4 kg/ha/yr, which were resulted from the global warming potential (GWP) of 14.5~21.7 $CO_2$/ha/year caused by the amount changes of rainfall, temperature, manure amendment, and fertilizer applied in fields. One distinct feature of the study result was that the changes of $N_2O-N$, $CH_4-C$ and $CO_2-C$ with future potential climate change simulation were varied by soil texture. Therefore it was concluded that there is a need to apply appropriate amount of manure amendment needs and to consider soil texture as well.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.83-96
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• 2018

The objective of this study was to estimate the climate change impact on water quantity and quality to Saemanguem watershed using SWAT (Soil and water assessment tool) model. The SWAT model was calibrated and validated using observed data from 2008 to 2017 for the study watershed. The $R^2$ (Determination coefficient), RMSE (Root mean square error), and NSE (Nash-sutcliffe efficiency coefficient) were used to evaluate the model performance. RCP scenario data were produced from 10 GCM (General circulation model) and all relevant grid data including the major observation points (Gusan, Jeonju, Buan, Jeongeup) were extracted. The systematic error evaluation of the GCM model outputs was performed as well. They showed various variations based on analysis of future climate change effects. In future periods, the MIROC5 model showed the maximum values and the CMCC-CM model presented the minimum values in the climate data. Increasing rainfall amount was from 180mm to 250mm and increasing temperature value ranged from 1.7 to $5.9^{\circ}C$, respectively, compared with the baseline (2006~2017) in 10 GCM model outputs. The future 2030s and 2070s runoff showed increasing rate of 16~29% under future climate data. The future rate of change for T-N (Total nitrogen) and T-P (Total phosphorus) loads presented from -26 to +0.13% and from +5 to 47%, respectively. The hydrologic cycle and water quality from the Saemanguem headwater were very sensitive to projected climate change scenarios so that GCM model should be carefully selected for the purpose of use and the tendency analysis of GCM model are needed if necessary.

• Journal of Environmental Impact Assessment
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• v.21 no.1
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• pp.71-80
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• 2012

Against the backdrop of the clear impact of climate change, it has become essential to analyze the influence of climate change and relevant vulnerabilities. This research involved evaluating the impact of heat waves in Seoul, from among many local autonomous bodies that are responsible for implementing measures on adapting to climate change. To carry out the evaluation, the A1B scenario was used to forecast future temperature levels. Future climate scenario results were downscaled to $1km{\times}1km$ to result in the incorporation of regional characteristics. In assessing the influence of heat waves on people-especially the excess mortality-we analyzed critical temperature levels that affect excess mortality and came up with the excess mortality. Results of this evaluation on the impact of climate change and vulnerabilities indicate that the number of days on which the daily average temperature reaches $28.1^{\circ}C$-the critical temperature for excess mortality-in Seoul will sharply increase in the 2050s and 2090s. The highest level of impact will be in the month of August. The most affected areas in the summer will be Songpa-gu, Gangnam-gu, and Yeongdeungpo-gu. These areas have a high concentration of residences which means that heat island effects are one of the reasons for the high level of impact. The excess mortality from heat waves is expected to be at least five times the current figure in 2090. Adaptation plan needs to be made on drawing up long-term adaptation measures as well as implementing short-term measures to minimize or adapt the impact of climate change.