• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.327-340
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• 2015

General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the spatio-temporal discrepancy between GCM and observed value, therefore, the models deliver output that are generally required calibration for applied studies. Which is generally done by Multi-Model Ensemble (MME) approach. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a MME of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.449-457
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• 2011

Recently, increasing heavy rainfalls due to climate change and/or variability result in hydro-climatic disasters being accelerated. To cope with the extreme rainfall events in the future, hydrologic frequency analysis is usually used to estimate design rainfalls in a design target year. The rainfall data series applied to the hydrologic frequency analysis is assumed to be stationary. However, recent observations indicate that the data series might not preserve the statistical properties of rainfall in the future. This study incorporated the residual analysis and the hydrologic frequency analysis to estimate design rainfalls in a design target year considering the non-stationarity of rainfall. The residual time series were generated using a linear regression line constructed from the observations. After finding the proper probability density function for the residuals, considering the increasing or decreasing trend, rainfalls quantiles were estimated corresponding to specific design return periods in a design target year. The results from applying the method to 14 gauging stations indicate that the proposed method provides appropriate design rainfalls and reduces the prediction errors compared with the conventional rainfall frequency analysis which assumes that the rainfall data are stationary.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Mechanical Science and Technology
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• v.16 no.2
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• pp.219-226
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• 2002

Natural gas is considered to be a promising alternative fuel for passenger cars, truck transportation and stationary engines providing positive effects both on the environment and energy security. However, since the composition of natural gas fuel varies with location, climate and other factors, it is anticipated that such changes in fuel properties will affect emission characteristics and performance of CNG (Compressed Natural Gas) engines. The purpose of the present study is to investigate the effects of the difference in gas composition on the engine performance and emission characteristics. The results show that THC (Total Hydrocarbon) decreases with increasing Wl (Wobbe Index) and MCP (Maximum Combustion Potential). On the other hand, it is observed that NOx slightly increases as Wl and MCP increase. The TLHV (Total Lower Heating Value of Intake) is proposed in this study as a potential index for compatibility of gas fuels in a CNG engine. There is a variation in power up to 20% depending on the composition of gas when the A/F ratio and spark timing are flexed for a specific gas fuel.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.3
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• pp.193-202
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• 2014

This paper presents a new method of sampling the climatic data for infrared signature analysis. Historical hourly data from a stationary marine buoy of KMA(Korean Meteorological Administration) are used to select a small number of sample points (N=100) to adequately cover the range of statistics(PDF, CDF) displayed by the original data set (S=56,670). The method uses a coarse bin to subdivide the variable space ($3^5$=243 bins) to make sample points cover the original data range, and a single-point ranking system to select individual points so that uniform coverage (1/N = 0.01) is obtained for each variable. The principal component analysis is used to calculate a joint probability of the coupled climatic variables. The selected sample data show good agreement to the original data set in statistical distribution and they will be used for statistical analysis of infrared signature and susceptibility of naval ships.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.185-185
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• 2020

기후변화의 영향으로 국지적 규모의 홍수, 가뭄 등의 피해 규모가 증가하고 있으며, 복사에너지 변화에 기인한 전지구적 대류활동의 변화는 단발성 피해에 확산되어 특정 지역의 기후 패턴 변화로 이어질 수 있다. 대류활동의 변화는 국가별 물순환의 변화로 이어질 수 있으며, 이로 인한 수자원의 변동성은 국가적 수자원 이용에 있어 중요한 요소로 작용될 수 있다. 수자원의 중요성으로 인해 국제적인 기관들은 전지구적 대류활동에 기인한 물순환 과정을 파악하고자 노력하였으며, 그 일환으로 GCMs (Global climate modeling) 등과 같은 모형이 개발되었고, 위성을 통한 전지구 강우량 측정망을 구축하였다. 위성을 통한 전구 강우량 자료와 GCMs에서 산출된 대류과정과 연관된 기후변량 자료들은 빅데이터로 구축되어 제한 없이 제공되고 있다. 정상성 강우 모의 기법은 데이터에 한정된 패턴을 반영하는 모형들로서 기후변화로 인한 기후 변동성 증가를 반영하는데 한계가 존재한다. 본 연구에서는 기상 빅데이터 자료를 기반으로 한반도의 강우량과 기상학적 특성을 연관할 수 있는 머신러닝의 일종인 딥러닝 방법을 접목시킨 강우 모의 기법을 적용하였다. 본 연구의 모형은 기후변화로 인한 기상학적 패턴의 변화를 딥러닝 기법을 통해 식별하고 식별된 기상학적 특성에 기반한 한반도의 강우량을 모의할 수 있다. 본 모형은 단기 및 장기 예측 모형과 결합하여 불확실성을 고려한 단/장기 강우량 평가에 활용될 수 있을 것으로 기대된다.

• Journal of the Korean Society of Urban Environment
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• v.18 no.4
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• pp.485-494
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• 2018

This study aims to identify the characteristics of oxidation and chemical composition of PM in winter season, 2017 at Incheon area. The mean concentration of air pollutants were $46{\pm}22{\mu}g/m^3-PM_{10}$, $29{\pm}18{\mu}g/m^3/-PM_{2.5}$, $5{\pm}3ppb-SO_2$, $0.56{\pm}0.24ppm-CO$, $21{\pm}13ppb-O_3$ and $28{\pm}17ppb-NO_2$, respectively. The dominant ion of the $PM_{1.0}$ chemical component were organic with $3.2{\mu}g/m^3$ and nitrate with $1.9{\mu}g/m^3$. The day and night variation of the $PM_{1.0}$ chemical components was higher in nighttime than those of daytime. The averaged nitrate oxidation rate (SOR) was 0.06 and sulfate oxidation rate was 0.11 during the field campaign. In the high mass loading period, nitrate oxidation rate (NOR) was up to 0.6 and also the nitrate in $PM_{1.0}$ was increased. The averaged ratio of $NO_x/SO_2$ was 8.7 and nitrate/sulfate was 3.1, respectively. In this results, the nitrate component in $PM_{1.0}$ was influenced by NOx from the stationary source as power plant and the mobile source around the measurement site.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.205-215
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• 2022

Amidst the global climate crisis, dam operation policies formulated under the stationary climate assumption could lead to unsatisfactory water management. In this work, we assessed status-quo performance of the Yongdam Dam in Korea under various climatic stresses in flood risk reduction and water supply reliability for 2021-2040. To this end, we employed a decision-centric framework equipped with a stochastic weather generator, a conceptual streamflow model, and a machine-learning reservoir operation rule. By imposing 294 climate perturbations to dam release simulations, we found that the current operation rule of the Yongdam dam could redundantly secure water storage, while inefficiently enhancing the supply reliability. On the other hand, flood risks were likely to increase substantially due to rising mean and variability of daily precipitation. Here, we argue that the current operation rules of the Yongdam Dam seem to be overly focused on securing water storage, and thus need to be adjusted to efficiently improve supply reliability and reduce flood risks in downstream areas.

• Journal of the Korean Geographical Society
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• v.43 no.3
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• pp.324-336
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• 2008

This paper examines the recent changes of summer precipitation in the aspect of temporal and spatial features using long-term($1958{\sim}2007$) observed station data over South Korea. tong-term mean summer precipitation has revealed two precipitation peaks during summer(June to September); one is the Changma as the first peak, and the other is the post-Changma as the second peak. During the Changma period, the spatial distribution of the maximum precipitation areas is determined by the prevailing southwesterlies and the quasi-stationary front, which results in large amount of precipitation at the windward side of mountain regions over South Korea. However during the post-Changma period, the spatial distribution of the maximum precipitation areas is determined by the lower tropospheric circulation flows from the west and the southeast around the Korean peninsula, and the weather phenomena such as Typhoons, convective instability, and cyclones which are originated from the Yangtze river. The larger amount of precipitation is founded on the southern coastal region and mountain and coastal areas in Korea during the second peak. Time series of total summer precipitation shows a steady increase and the increasing trend is more obvious during the recent 10 years. Decadal variation in summer precipitation indicates a large increase of precipitation, especially in the recent 10 years both in the Changma and the post-Changma period. However, the magnitude of change and the period of the maximum peak presents remarkable contrasts among stations. The most distinct decadal change occurs at Seoul, Busan, and Gangnueng. The precipitation amount is increasing significantly during the post-Changma period at Gangnueng, while the precipitation increases in the period between two maximum precipitation peaks during summer at Seoul and Busan.