• Journal of Climate Change Research
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• v.7 no.1
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• pp.69-75
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• 2016

Recently, many countries all over the world have been suffered from disaster caused by climate change. Especially in case of developed countries, the disaster is concentrated in the industry sector. In this research, we analyzed industrial vulnerable homogeneous hotspot for the climate change using spatial autocorrelation analysis on the south Korea. Homogeneous hot spot areas through autocorrelation analysis indicate the spatial pattern of areas interacted each other. Industry sector have responsibility of green house gas emissions, and should adapt to the climate change caused by greenhouse gas already released. So, we integrated the areas sensitive to mitigation option with the areas hardly adapt to climate change because of vulnerable infrastructure. We expected that the result of this research could contribute to the decision-making system of climate change polices.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.1
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• pp.119-126
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• 2020

This study analyzed the effects of future climate change on water resources in the Gyeongan-cheon watershed of the Han River. Considering the uncertainties of GCM climate data, future data using 16 GCMs and SQM downscaling method are used. And SWAT model was applied to simulate the hydrological changes from the past to the future. The maximum to minimum difference according to GCM for the future period (2010-2099) was about 1,500 mm of annual precipitation, 150 mm of evapotranspiration, 1,380 mm of runoff, and the deviation from the mean was -40 % to +60 % of precipitation, ±15 % of evapotranspiration, -60 % to +90 % of runoff, which means that the variability is very high according to GCM. The impacts of climate change over the three future periods showed that precipitation, evapotranspiration, and runoff were expected to increase gradually toward the far future (2070-2099), and would be relatively larger under the RCP 8.5 scenario. On a monthly basis, it was analyzed that precipitation and runoff increased in July to September, while the evapotranspiration decreased in July and August, and increased in September and October. The results of this study are expected to be helpful in understanding the future climate impacts of various GCM data and the uncertainties associated with GCMs.

• Journal of Korea Water Resources Association
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• v.39 no.9 s.170
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• pp.755-766
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• 2006

Since the accuracy of climate forecast information has improved from better understanding of the climatic system, particularly, from the better understanding of ENSO and the improvement in meteorological models, the forecasted climate information is becoming the important clue for streamflow prediction. This study investigated the available climate forecast information to improve the extended streamflow prediction in Korea, such as MIMI(Monthly Industrial Meteorological Information) and GDAPS(Global Data Assimilation and Prediction) and measured their accuracies. Both MIMI and the 10-day forecast of GDAPS were superior to a naive forecasts and peformed better for the flood season than for the dry season, thus it was proved that such climate forecasts would be valuable for the flood season. This study then forecasted the monthly inflows to Chungju Dam by using MIMI and GDAPS. For MIMI, we compared three cases: All, Intersection, Union. The accuracies of all three cases are better than the naive forecast and especially, Extended Streamflow Predictions(ESPs) with the Intersection and with Union scenarios were superior to that with the All scenarios for the flood season. For GDAPS, the 10-day ahead streamflow prediction also has the better accuracy for the flood season than for the dry season. Therefore, this study proved that using the climate information such as MIMI and GDAPS to reduce the meteorologic uncertainty can improve the accuracy of the extended streamflow prediction for the flood season.

• Journal of Korea Water Resources Association
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• v.53 no.9
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• pp.647-660
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• 2020

The objectives of this study are to develop a framework for selecting multi-GCMs considering Asia monsoon characteristics and assess it's applicability. 12 climate variables related to monsoon climates are selected for GCM selection. The framework for selecting multi-GCMs includes the evaluation matrix of GCM performance based on their capability to simulate historical climate features. The climatological patterns of 12 variables derived from individual GCM over the summer monsoon season during the past period (1976-2005) and they are compared against observations to evaluate GCM performance. For objective evaluation, a rigorous scoring rule is implemented by comparing the GCM performance based on the results of statistics between historical simulation derived from individual GCM and observations. Finally, appropriate 5 GCMs (NorESM1-M, bcc-csm1-m, CNRM-CM5, CMCC-CMS, and CanESM2) are selected in consideration of the ranking of GCM and precipitation performance of each GCM. The selected 5 GCMs are compared with the historical observations in terms of monsoon season and monthly mean to validate their applicability. The 5 GCMs well capture the observational climate characteristics of Asia for the 12 climate variables also they reduce the bias between the entire GCM simulations and the observational data. This study demonstrates that it is necessary to consider various climate variables for GCM selection and, the method introduced in this study can be used to select more reliable climate change scenarios for climate change assessment in the Asia region.

• Journal of Wetlands Research
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• v.15 no.1
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• pp.125-137
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• 2013

Global warming and climate change have influence on abnormal weather pattern and the rainstorm has a localized and intensive tendency in Korea. IPCC(2007) also reported the rainstorm and typhoon will be more and more stronger due to temperature increase during the 21st century. Flood Estimation Handbook(Institute of Hydrology, 1999) published in United Kingdom, in the case that the data period is shorter than return period, recommends the regional frequency analysis rather than point frequency analysis. This study uses Regional Climate Model(RCM) of Korea Meteorological Administration(KMA) for obtaining the rainfall and for performing the regional frequency analysis. We used the rainfall data from 58 stations managed by KMA and used L-moment algorithm suggested by Hosking and wallis(1993) for the regional frequency analysis considering the climate change. As the results, in most stations, the rainfall amounts in frequencies have an increasing tendency except for some stations. According to the A1B scenario, design rainfall is increased by 7~10% compared with the reference period(1970-2010).

• Journal of Climate Change Research
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• v.3 no.1
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• pp.1-12
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• 2012

In order to use as basic data of adaptation, this study focused on a 'Water management vulnerability estimation' in Korea. Vulnerability is estimated dividing into flood mitigation and water resource management. Temporal resolution is 2000 year and the future 2020 year, 2050 year, 2100 year via A1B scenario. Time series data was normalized. Then weight that is gotten through delphi investigation was multiplied. Vulnerability is calculated through this process. In flood mitigation vulnerability, it was estimated to adaptation ability affect relatively biggest influence. In future, some area of Gangwon-do was analyzed that the flood mitigation vulnerability increases. In water resource management, it was estimated to climate exposure affect relatively biggest influence. At 2020 yr, there is a trend toward increased in the Chungcheongbuk-do and DaeJeon, Daegu, some area of Gyeongsangnamdo. Because this study evaluate relative vulnerability of whole country and analyzed spatial distribution, when local government establishes climate change adaptation details enforcement countermeasure, this study can give help to grasp whether should invest more in some field.

• Journal of Climate Change Research
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• v.4 no.1
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• pp.11-26
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• 2013

This study examined the applicability of robust decision making (RDM) over standard decision making (SDM) by comparing each result of water supply planning under climate change uncertainties for a Korean dam case. RDM determines the rank of alternatives using the regret criterion which derives less fluctuating alternatives under the risk level regardless of scenarios. RDM and SDM methods were applied to assess hypothetic scenarios of water supply planning for the Andong dam and Imha dam basins. After generating various climate change scenarios and six assumed alternatives, the rank of alternatives was estimated by RDM and SDM respectively. As a result, the average difference in the rank of alternatives between RDM and SDM methods is 0.33~1.33 even though the same scenarios and alternatives were used to be ranked by both of RDM and SDM. This study has significance in terms of an attempt to assess a new approach to decision making for responding to climate change uncertainties in Korea. The effectiveness of RDM under more various conditions should be verified in the future.

• Journal of Climate Change Research
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• v.9 no.1
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• pp.13-29
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• 2018

This study presented evaluation procedure for selecting appropriate GCMs and downscaling method by focusing on the climate extreme indices suitable for climate change adaptation. The procedure includes six stages of processes as follows: 1) exclusion of unsuitable GCM through raw GCM analysis before bias correction; 2) calculation of the climate extreme indices and selection of downscaling method by evaluating reproducibility for the past and distortion rate for the future period; 3) selection of downscaling method based on evaluation of reproducibility of spatial correlation among weather stations; and 4) MME calculation using weight factors and evaluation of uncertainty range depending on number of GCMs. The presented procedure was applied to 60 weather stations where there are observed data for the past 30 year period on Korea Peninsula. First, 22 GCMs were selected through the evaluation of the spatio-temporal reproducibility of 29 GCMs. Between Simple Quantile Mapping (SQM) and Spatial Disaggregation Quantile Delta Mapping (SDQDM) methods, SQM was selected based on the reproducibility of 27 climate extreme indices for the past and reproducibility evaluation of spatial correlation in precipitation and temperature. Total precipitation (prcptot) and annual 1-day maximum precipitation (rx1day), which is respectively related to water supply and floods, were selected and MME-based future projections were estimated for near-future (2010-2039), the mid-future (2040-2069), and the far-future (2070-2099) based on the weight factors by GCM. The prcptot and rx1day increased as time goes farther from the near-future to the far-future and RCP 8.5 showed a higher rate of increase in both indices compared to RCP 4.5 scenario. It was also found that use of 20 GCM out of 22 explains 80% of the overall variation in all combinations of RCP scenarios and future periods. The result of this study is an example of an application in Korea Peninsula and APCC Integrated Modeling Solution (AIMS) can be utilized in various areas and fields if users want to apply the proposed procedure directly to a target area.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.105-112
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• 2014

The main purpose of this study is to measure spatio-temporal variation of forest tree volume based on the RCP(Representative Concentration Pathway) 8.5 scenario, targeting on Pinus densiflora forests which is the main tree species in South Korea. To estimate nationwide scale, $5^{th}$ forest type map and National Forest Inventory data were used. Also, to reflect the impact of change in place and climate on growth of forest trees, growth model reflecting the climate and topography features were applied. The result of the model validation, which compared the result of the model with the forest statistics of different cities and provinces, showed a high suitability. Considering the continuous climate change, volume of Pinus densiflora forest is predicted to increase from $131m^3/ha$ at present to $212.42m^3/ha$ in the year of 2050. If the climate maintains as the present, volume is predicted to increase to $221.92m^3/ha$. With the climate change, it is predicted that most of the region, except for some of the alpine region, will have a decrease in growth rate of Pinus densiflora forest. The growth rate of Pinus densiflora forest will have a greater decline, especially in the coastal area and the southern area. With the result of this study, it will be possible to quantify the effect of climate change on the growth of Pinus densiflora forest according to spatio-temporal is possible. The result of the study can be useful in establishing the forest management practices, considering the adaptation of climate change.

• Journal of Science Education
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• v.38 no.2
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• pp.430-442
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• 2014

The purpose of this study is to investigate the Preconception about the causes and consequences of climate change. Participants were 196 High school students who live in Seoul and Gyeong-gi Province. And data was collected through questionnaire to confirm preconception about the causes and consequences of Climate Change. After confirming with students preconceptions, 10 people randomly selected and Semi-structured interview were conducted. The findings is students did not know exactly about Ozone depletion, global warming, the greenhouse effect factors and causal relationships. And we can confirm that the student's preconception is affecting.