• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.99-99
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• 2022

Jeju island has a humid subtropical climate and this climate zone is expected to migrate northward toward the main land, Korea Peninsula, as temperature increases are accelerated. Vegetation type has been inevitably shifted along with the climatic change, having more subtropical species native in southeast Asia or even in Africa. With the forest composition shift, it becomes more important than ever to analyze the water balance of the forest wihth the ongoing as well as upcoming climate change. Here, we implemented the Ecosystem Demography Biosphere Model (ED2) by initializing the key variables using forest inventory data (diameter at breast height in 2012). Out of 10,000 parameter sets randomly generated from prior distribution distributions of each parameter (i.e., Monte-Carlo Method), we selected four behavioral parameter sets using remote-sensing data (LAI-MOD15A2H, GPP-MOD17A2H, and ET-MOD16A2, 8-days at 500-m during 2001-2005), and evaluated the performances using eddy-covariance carbon flux data (2012 Mar.-Sep. 30-min) and remote sensing data between 2006-2020. We simulated each of the four RCP scenarios (2.6, 4.5, 6.0, and 8.5) from four climate forcings (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5 from ISIMIP2b). Based on those 64 simulation sets, we estimate the changes in water balance resulting from the forest composition shift, and also uncertainty in the estimates and the sensitivity of the estimates to the parameters, climate forcings, and RCP scenarios.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2016년도 추계 학술발표논문집
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• pp.1-5
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• 2016

• 한국습지학회지
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• 제21권spc호
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• pp.51-60
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• 2019

본 연구에서는 기후변화 시나리오 자료를 이용하여 지점빈도해석(At-site Frequency Analysis, AFA)과 지역빈도해석(Regional Frequency Analysis, RFA) 등을 수행하였고, Monte Carlo simulation을 통한 RRMSE(relative root mean squared error) 값을 비교·분석함으로써 각 빈도해석 방법에 따른 성능을 평가하고자 하였다. 확률강우량 산정을 위하여 기상청에서 국가표준시나리오로 제공하는 RCM(Regional Climate Model) 자료 중 하나인 HadGEM3-RA(12.5km) 기후모델 자료로부터 우리나라 615개 지점에 대한 일 강우 자료를 추출하였고, 자료의 편의보정(bias correction)과 공간상세화(spatial disaggregation)를 위하여 분위사상법(quantile mapping)과 역거리제곱법(inverse distance squared method)을 적용하였다. 분석 결과 지역빈도해석 방법이 지점빈도해석보다 정확하게 확률강우량을 산정하는 것으로 나타났으며, 이는 기후변화 시나리오 기반의 확률강우량 산정시 지역빈도해석의 결과가 보다 합리적인 전망 결과를 도출할 것으로 판단된다.

• 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.

• 한국전자통신학회논문지
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• 제11권2호
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• pp.125-138
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• 2016

현재 기상정보를 활용하고자 하는 수요는 증가하고 있으나 제공되는 기상기후정보는 한정적이다. 개선을 위해서는 기상기후 빅데이터 활용 플랫폼 지원, 정부 및 공공기관, 기업의 요구에 부응하는 기상기후 빅데이터 전문 인력 양성이 필요하다. 기상기후 빅데이터는 다양한 분야에서 활용 가능한 고부가가치 서비스 제공이 필요하며 서비스의 확대 및 새로운 콘텐츠 개발을 위하여 산업 분야별 유형별 특성에 맞는 맞춤형 서비스가 제공되어야 한다. 맞춤형 서비스를 제공하기 위해서는 국가차원의 협업생태계 조성이 필수적이다. 협업생태계 조성 환경을 만들기 위해서는 해양정책과 기상정책의 융합, 해양학과 기상학의 융합, R&D 기초연구와 응용연구의 융합이 필요하다. 이후 수요분석, 생산정보공유, 단일화 순서로 협업생태계를 구축할 수 있다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.252-252
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• 2011

Rainfall-runoff models are calibrated and validated by using a same data set such as observations. The past climate change effects the present rainfall pattern and also will effect on the future. To predict rainfall-runoff more preciously we have to consider the climate change pattern in the past, present and the future time. Thus, in this study, the climate change represents changes in mean precipitation and standard deviation in different patterns. In some river basins, there is no enough length of data for the analysis. Therefore, we have to generate the synthetic data using proper distribution for calculation of precipitation based on the observed data. In this study, Kajiyama model is used to analyze the runoff in the dry and the wet period, separately. Mean and standard deviation are used for generating precipitation from the gamma distribution. Twenty hypothetical scenarios are considered to show the climate change conditions. The mean precipitation are changed by -20%, -10%, 0%, +10% and +20% for the data generation with keeping the standard deviation constant in the wet and the dry period respectively. Similarly, the standard deviations of precipitation are changed by -20%, -10%, 0%, +10% and +20% keeping the mean value of precipitation constant for the wet and the dry period sequentially. In the wet period, when the standard deviation value varies then the mean NSE ratio is more fluctuate rather than the dry period. On the other hand, the mean NSE ratio in some extent is more fluctuate in the wet period and sometimes in the dry period, if the mean value of precipitation varies while keeping the standard deviation constant.

• 한국태양에너지학회 논문집
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• 제29권5호
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• pp.81-87
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• 2009

Computer simulation of buildings and solar energy systems is being used increasingly in energy assessments and design.. Building designers often now predict the performance of buildings simulation programmes that require hourly weather data. However, not all weather stations provide hourly data. Climate prediction models such as HadCM3 also provide the daily average dry bulb temperature as well as the maximum and minimum. Hourly temperature values are available for building thermal simulations that accounts for future changes to climate. In order to make full use of these predicted future weather data in building simulation programmes, algorithms for downscaling daily values to hourly values are required. This paper describes a more accurate method for generating hourly temperature values in the South Korea that uses all three temperature parameters from climate model. All methods were evaluated for accuracy and stability in terms of coefficient of determination and cumulative error. They were compared with hourly data collected in Seoul and Ulsan, South Korea.

• 한국지구과학회지
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• 제42권4호
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• 대한지구과학교육학회지
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• 제4권3호
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• pp.199-204
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• 2011

This study examines eighteen elementary teachers knowledge and teaching practrice of climate change using the KQEM survey, modified from the survey developed by Leiserowitz, A., Smith, N. & Marlon, J.R. (2010). The survey includes 11 questions from KQEM survey and 2 open ended questions about teachers' knowledge of climate change and their understandings of important climate change concept for elementary students. All of the participant teachers were purposefully selected for the study and were participated in the study volunteerly. The data for this study were analyzed both quantitatively and qualitatively. The result of this study indicates that the teachers have knowledge of climate change specifically about the topics of causes of climate change and consequences of climate change such as shifting biome and ecological impacts. While most of the teachers described climate change phenomena using scientific knowledge, some of the teachers (N=2) showed misconceptions about climate change phenomena. Most of the teachers thought the causes of climate change and potential solutions to reduce climate change are important concept that elementary students need to understand about climate change. Actually, most of the teachers are currently teaching the causes and consequences of climate change (N=13) potential solutions to global warming (N= 8). This study could inform teacher educators about what elementary teachers understand about climate change and what elementary teachers are currently teaching about climate change.

• 간호행정학회지
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• 제12권4호
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• pp.564-573
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• 2006

Purposes: This study was to provide basic data to explain the effect of the organizational effectiveness factor on hospital nursing, to construct an appropriate model to examine the validation and relationship with variables and to provide basic data for improving the organizational effectiveness of hospital nursing. Method: This study was a descriptive correlation research. Subjects of the study were 348 nurses, 219 patients, and 89 nurses for nursing quality. Twelve measurement variables and nine paths were established in the hypothetical model. Results: The fitness indices of the model were GFI=0.91, NFI=0.90, and PGFI=0.49. Five among the nine paths proved to be statistically significant : level of nurse manpower to organizational effectiveness, conflict to organizational effectiveness, organizational climate to organizational effectiveness, level of nurse manpower to organizational climate, and leadership to organizational climate. Level of nurse manpower and leadership influenced organizational climate. Organizational climate accounted for 43% by the predictor variables, and the level of nurse manpower, conflict, and organizational climate influenced the organizational effectiveness, which accounted for 77% by the predictor variables. Conclusion: This study identified that the level of nurse manpower, leadership, conflict, and organizational climate are important factors affecting organizational effectiveness.