• 환경영향평가
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• 제22권1호
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• pp.39-50
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• 2013

Occurrence of landslides has been increasing due to extreme weather events(e.g. heavy rainfall, torrential rains) by climate change. Pyeongchang, Korea had seriously been damaged by landslides caused by a typhoon, Ewiniar in 2006. Moreover, the frequency and intensity of landslides are increasing in summer due to torrential rain. Therefore, risk assessment and adaptation measure is urgently needed to build resilience. To support landslide adaptation measures, this study predicted landslides occurrence using MaxEnt model and suggested susceptibility map of landslides. Precipitation data of RCP 8.5 Climate change scenarios were used to analyze an impact of increase in rainfall in the future. In 2050 and 2090, the probability of landslides occurrence was predicted to increase. These were due to an increase in heavy rainfall and cumulative rainfall. As a result of analysis, factors that has major impact on landslide appeared to be climate factors, prediction accuracy of the model was very high(92%). In the future Pyeongchang will have serious rainfall compare to 2006 and more intense landslides area expected to increase. This study will help to establish adaptation measure against landslides due to heavy rainfall.

• 한국농림기상학회지
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• 제24권3호
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• pp.155-163
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• 2022

본 연구에서는 벼의 생물계절 예측 모형을 예시로 하여 해당 모형의 구동에 필요한 맞춤형 앙상블 상세기후예측자료를 구축하고 해당 자료의 보정방법을 고도화 하였을 때 농업적 활용 분야에서 가지는 부가가치를 확인해 보았다. 이를 위해, 벼의 생물계절 모의를 위해 집중적으로 필요한 기상자료인 1~10월의 일 평균/최저/최고 기온의 앙상블 장기(6개월) 전망자료를 생산하고 해당자료의 질을 높이기 위해 분위사상법 기반의 보정방법의 개선을 수행하였다. 그 결과 최저/최고/평균 기온 모두 대부분의 월에서 20일을 버퍼기간으로 선정하였을 때 4.51~15.37%까지 RMSE가 감소하는 것을 확인하였으며, 8~10월은 변수 및 월 별로 최적 버퍼기간이 다른 것을 확인하였다. 또한, 이러한 기상학적 변수의 개선은 벼의 생육단계별 시작일 예측이 모든 단계에서 7.82~10.60% 감소하였으며, 61개 ASOS 지점 가운데서도 생육단계에 따라 75~100%의 지점에서 RMSE가 감소하는 결과를 확인하였다. 본 연구 결과는 벼의 생물계절뿐만 아니라 감자, 고구마, 옥수수 등 타 작물로의 적용도 가능할 것으로 생각된다. 나아가, 일조시간, 습도, 풍속과 같은 예측변수들의 보정자료가 구축되면 농산물 작황전망, 병해충 예찰 등 다양한 분야의 학제간 연구에 적용하여 더 많은 부가가치 창출이 가능할 것으로 기대된다.

• 한국농공학회논문집
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• 제61권6호
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• pp.81-92
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• 2019

The objective of this study is to evaluate hydrologic impacts of climate change according to downscaling methods using the Soil and Water Assessment Tool (SWAT) model at watershed scale. We used the APCC Integrated Modeling Solution (AIMS) for assessing various General Circulation Models (GCMs) and downscaling methods. AIMS provides three downscaling methods: 1) BCSA (Bias-Correction & Stochastic Analogue), 2) Simple Quantile Mapping (SQM), 3) SDQDM (Spatial Disaggregation and Quantile Delta Mapping). To assess future hydrologic responses of climate change, we adopted three GCMs: CESM1-BGC for flood, MIROC-ESM for drought, and HadGEM2-AO for Korea Meteorological Administration (KMA) national standard scenario. Combined nine climate change scenarios were assessed by Expert Team on Climate Change Detection and Indices (ETCCDI). SWAT model was established at the Mankyung watershed and the applicability assessment was completed by performing calibration and validation from 2008 to 2017. Historical reproducibility results from BCSA, SQM, SDQDM of three GCMs show different patterns on annual precipitation, maximum temperature, and four selected ETCCDI. BCSA and SQM showed high historical reproducibility compared with the observed data, however SDQDM was underestimated, possibly due to the uncertainty of future climate data. Future hydrologic responses presented greater variability in SQM and relatively less variability in BCSA and SDQDM. This study implies that reasonable selection of GCMs and downscaling methods considering research objective is important and necessary to minimize uncertainty of climate change scenarios.

• 한국환경복원기술학회지
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• 제9권5호
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• pp.32-40
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• 2006

This study's objects are to suggest effective forest community-level management measures by identifying the vulnerable forest vegetation communities types to climate change through a comparative analysis with present forest communities identified and delineated in the Actual Vegetation Map. The methods of this study are to classify the climatic life zones based on the correlative climate-vegetation relationship for each forest vegetation community, the Holdridge Bio-Climate Model was employed. This study confirms relationship between forest vegetation and environmental factors using Pearson's correlation coefficient analysis. Then, the future distribution of forest vegetation are predicted derived factors and present distribution of vegetation by utilizing the multinomial logit model. The vulnerability of forest to climate change was evaluated by identifying the forest community shifts slower than the average velocity of forest moving (VFM) for woody plants, which is assumed to be 0.25 kilometers per year. The major findings in this study are as follows : First, the result of correlative analysis shows that summer precipitation, mean temperature of the coldest month, elevation, soil organic matter contents, and soil acidity (pH) are highly influencing factors to the distribution of forest vegetation. Secondly, the result of the vulnerability assessment employing the assumed velocity of forest moving for woody plants (0.25kmjyear) shows that 54.82% of the forest turned out to be vulnerable to climate change. The sub-alpine vegetations in regions around Mount Jiri and Mount Seorak are predicted to shift the dominance toward Quercus mongolica and Pinus densiflora communities. In the identified vulnerable areas centering the southern and eastern coastal regions, about 8.27% of the Pinus densiflora communities is likely to shift to sub-tropical forest communities, and 3.38% of the Quercus mongolica communities is likely to shift toward Quercus acutissima communities. In the vulnerable areas scattered throughout the country, about 8.84% of the Quercus mongolica communities is likely to shift toward Pinus densiflora communities due to the effects of climate change. The study findings concluded that challenges associated with predicting the future climate using RCM and the assessment of the future vulnerabilities of forest vegetations to climate change are significant.

• Ecology and Resilient Infrastructure
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• 제8권3호
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• pp.154-163
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• 2021

본 연구는 최근 국내에 귀화식물로 기록된 미국쥐손이의 적합 서식지의 분포에 영향을 미치는 요인을 파악하고, 미래의 변화를 예측하고자 수행되었다. 전국을 대상으로 총 68개 지점에서 미국쥐손이의 출연 자료를 수집하고 MaxEnt 모델을 적용하여 기준년대(1981~2010)와 기후시나리오에 따른 미래의 적합 서식지 분포를 예측했다. 미국쥐손이의 분포에는 강수량 계절성(bio15), 가장 따뜻한 분기의 평균기온(bio10), 가장 건조한 분기의 평균기온(bio09)가 크게 기여하는 것으로 나타났다. 기후변화에 따라 미국쥐손이의 높은 수준의 적합 서식지는 기준년도에 우리나라 면적의 6.43%를 차지하였고, 먼미래(2071~2100)에는 SSP2-4.5 하에서 92.60%까지, SSP5-4.8 하에서 98.36%까지 차지하는 것으로 예측되었다.

• 환경영향평가
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• 제23권2호
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• pp.101-111
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• 2014

The research was carried out in order to find climate factors which determine the distribution of Neolitsea sericea, and the potential habitats (PHs) under the current climate and three climate change scenario by using species distribution models (SDMs). Four climate factors; the minimum temperature of the coldest month (TMC), the warmth index (WI), summer precipitation (PRS), and winter precipition (PRW) : were used as independent variables for the model. Three general circulation models under A1B emission scenarios were used as future climate scenarios for the 2050s (2040~2069) and 2080s (2070~2099). Highly accurate SDMs were obtained for N. sericea. The model of distribution for N. sericea constructed by SDMs showed that minimum temperature of the coldest month (TMC) is a major climate factor in determining the distribution of N. sericea. The area above the $-4.4^{\circ}C$ of TMC revealed high occurrence probability of the N. sericea. Future PHs for N. sericea were projected to increase respectively by 4 times, 6.4 times of current PHs under 2050s and 2080s. It is expected that the potential of N. sericea habitats is expanded gradually. N. sericea is applicable as indicator species for monitoring in the Korean Peninsula. N. sericea is necessary to be monitored of potential habitats.

• 한국농공학회논문집
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• 제60권6호
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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.

• 한국농공학회논문집
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• 제63권4호
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• pp.65-86
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• 2021

This study aims at testing a hypothesis that the resilience of agricultural water systems is characterized by trade-offs and synergies of effects from climate and socioeconomic change. To achieve this, an Agricultural Water System Climate Resilience Assessment (ACRA) framework is established to evaluate comprehensive resilience of an agricultural water system to the combined impacts of the climate and socioeconomic changes with a case study in South Korea. Understanding dynamic behaviors of the agricultural water systems under climate and socioeconomic drivers is not straightforward because the system structure includes complex interactions with multiple feedbacks across components in water and agriculture sectors and climate and socioeconomic factors, which has not been well addressed in the existing decision support models. No consideration of the complex interactions with feedbacks in a decision making process may lead to counterintuitive and untoward evaluation of the coupled impacts of the climate and socioeconomic changes on the system performance. In this regard, the ACRA framework employs a System Dynamics (SD) approach that has been widely used to understand dynamics of the complex systems with the feedback interactions. In the ACRA framework applied to the case study in South Korea, the SD model works along with HOMWRS simulation. The ACRA framework will help to explore resilience-based strategies with infrastructure investment and management options for agricultural water systems.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.738-739
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• 2015

The main objective of this study, "Spatial Downscaling of Precipitation from GCMs for Assessing Climate Change over Han River and Imjin River Watersheds", is to carry out over Han River and Imjin River watersheds. To this end, a statistical regression method with MOS (Model Output Statistics) corrections at every downscaling step was developed and applied for downscaling the spatially-coarse Global Climate Model Projections (GCMPs) from CCSM3 and CSIRO with respect to precipitation into 0.1 degree (about 11 km) spatial grid over study regions. The spatially archived hydro-climate data sets such as Willmott, GsMap and APHRODITE datasets were used for MOS corrections by means of monthly climatology between observations and downscaled values. Precipitation values downscaled in this study were validated against ground observations and then future climate simulation results on precipitation were evaluated for the projections.

• 한국지리정보학회지
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• 제15권3호
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• pp.119-136
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• 2012

본 연구는 IPCC(Intergovernmental Panel on Climate Change)에서 제시한 기후변화 취약성 개념을 서울시에 적용, 적정 홍수 취약성 지표 산정 및 퍼지모형을 활용하여 기후변화 분야 중 홍수취약성을 평가하고 GIS를 이용하여 취약성도를 작성하였다. 이를 위해 선행연구를 기반으로 지표를 도출하였다. 도출된 지표는 기후노출(일 최대 강수량, 일강수량 80m 이상인 날 수), 민감도(침수지역, 경사, 지질, 고도, 하천으로부터의 거리, 지형, 토양 및 불투수면적) 및 적응능력(홍수조절능력, 자연녹지, 공원녹지) 등의 자료이며, 이를 GIS 기반의 공간데이터베이스로 구축하였다. 구축된 지표값들을 통합하기 위한 방법으로 퍼지모형을 활용했으며, 퍼지소속값 결정을 위해서는 빈도비를 활용하였다. 2010년 침수 발생 자료를 활용하여 항목들간의 상관관계 및 퍼지소속값을 산정하였으며, 2011년 침수 발생 지역으로 작성된 취약성도를 검증하였다. 분석결과 서울지역 홍수피해에 크게 영향을 미치는 지표는 일강수량이 80mm이상인 날수, 하천과의 거리, 불투 수층으로 나타났다. 서울의 경우, 최대강수량이 269mm 이상일 때 적응능력(유수지, 녹지)이 부족하고, 고도가 16~20m 정도이며 하천에서 50m이내에 인접한 지역, 공업용지에서 홍수취약성이 매우 높은 것으로 나타났다. 지역적으로 영등포구, 용산구, 마포구 등 한강 본류의 양안에 위치한 구들이 비교적 취약지역을 많이 포함하고 있는 것으로 나타났다. 본 연구는 기후변화 취약성 평가의 개념을 적용하고, 방법론으로 퍼지모형을 활용함으로써 기존의 취약성 평가기법을 개선하였으며 평가결과는 홍수예방정책에 대한 우선지역 선정과 의사결정의 주요한 근거로 활용될 수 있을 것으로 기대된다.