• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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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.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.1-5
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• 2016

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.51-60
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• 2019

In this study, we performed At-site Frequency Analysis(AFA) and Regional Frequency Analysis(RFA) using the observed and climate change scenario data, and the relative root mean squared error(RMMSE) was compared and analyzed for both approaches through Monte Carlo simulation. To evaluate the rainfall quantile, the daily rainfall data were extracted for 615 points in Korea from HadGEM3-RA(12.5km) climate model data, one of the RCM(Regional Climate Model) data provided by the Korea Meteorological Administration(KMA). Quantile mapping(QM) and inverse distance squared methods(IDSM) were applied for bias correction and spatial disaggregation. As a result, it is shown that the RFA estimates more accurate rainfall quantile than AFA, and it is expected that the RFA could be reasonable when estimating the rainfall quantile based on climate change scenarios.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.125-138
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• 2016

Currently, there is increasing demand for weather information, however, providing meteorology and climate information is limited. In order to improve them, supporting the meteorology and climate big data platform use and training the meteorology and climate big data specialist who meet the needs of government, public agencies and corporate, are required. Meteorology and climate big data requires high-value usable service in variety fields, and it should be provided personalized service of industry-specific type for the service extension and new content development. To provide personalized service, it is essential to build the collaboration ecosystem at the national level. Building the collaboration ecosystem environment, convergence of marine policy and climate policy, convergence of oceanography and meteorology and convergence of R&D basic research and applied research are required. Since then, demand analysis, production sharing information, unification are able to build the collaboration ecosystem.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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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.

• Journal of the Korean Solar Energy Society
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• v.29 no.5
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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.

• Journal of the Korean earth science society
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• v.42 no.4
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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.

• Journal of the Korean Society of Earth Science Education
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• v.4 no.3
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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.

• Journal of Korean Academy of Nursing Administration
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• v.12 no.4
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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.