• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.143-146
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• 2016

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.154-154
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• 2022

The frequency of natural disasters, including floods and drought events, driven by climate change has increased in recent times. Investigating the climate regimes and the roles of climate variables are indispensable to forestall future climate change-related disasters. This study compares the variability of two popular and widely used climate indices i.e., the United Nations Environment Programme (UNEP) aridity index and the Modified De-Martonne (MDM) index to assess the trend of climate change in the Chungcheong provinces of South Korea. The trend of annual and monthly climate indices was conducted using a non-parametric Mann-Kendall test and Kolmogorov-Smirnov normality test with daily climate data of 48 years (1978-2020) from 10 synoptic stations. The findings indicate that UNEP and MDM indices had a wet climate regime for the annual trend, with the UNEP index indicating a relatively humid trend of 60% humid, 20% semi-arid, and 10% sub-humid for the 48-years study period. However, the MDM index showed a high frequency of a severe wet climatic condition followed by the semi-arid condition. The months of July and August had the highest occurring frequency of the wet climatic condition (90%) for both UNEP and MDM indices. Comparing the two provinces, Chungnam showed a relatively wetter climatic condition using the UNEP index, while the MDM index indicated no significant regional difference in climate regime between the two provinces. The Kolmogorov-Smirnov normality test showed that all the 10 stations are normally distributed for monthly climate conditions at a 5% significant level in the two provinces except five stations for UNEP index and four stations for MDM index in the month of January.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.1-11
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• 2017

The analysis of baseflow contribution is very significant in Korea because most rivers have high variability of streamflow due to the monsoon climate. Recently, the importance of such analysis is being more evident especially in terms of river management because of the changing pattern of rainfall and runoff resulted from climate change. Various baseflow separation methods have been developed to separate baseflow from streamflow. However, it is very difficult to identify which method is the most accurate way due to the lack of measured baseflow data. Moreover, it is inappropriate to analyze the annual baseflow contribution for Korean rivers because rainfall patterns varies significantly with the seasons. Thus, this study compared the baseflow contributions at various time-scales (annual, seasonal and monthly) for the 4 major river basins through BFI (baseflow index) and suggested baseflow contribution of each basin by the BFI ranges searched from different baseflow separation methods (e.g., BFLOW, HYSEP, PART, WHAT). Based on the comparison of baseflow contributions at the three time scales, this study showed that the baseflow contributions from the monthly and seasonal analysis are more reasonable than that from the annual analysis. Furthermore, this study proposes that defining BFI with its range is more proper than a specific value for a watershed, considering the difference of BFIs between various baseflow separation methods.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.2
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• pp.149-166
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• 2013

The purpose of this study is to analyze the regional disparity and its determinants of $CO_2$ emission from the residential energy consumption in China. This study examines factors that affect the $CO_2$ emission per capita using the panel model. The panel model was set by a balanced panel data for 30 provinces and for the period of 2006~2011. $CO_2$ emission per capita is used as the dependent variable and characteristics of the household and regional physical environmental factors are selected as the explanatory variables. The important findings can be summarized as follows. $CO_2$ emission per capita is influenced by the ratio of the graduate students, household size, the ratio of the old-aged, female economic participation rate. High residential density is negatively affected on $CO_2$ emission. The findings suggest that the effect of policies reducing $CO_2$ emission per capita may vary by characteristics of the household, energy sources and regional climate. The results of this empirical study give some implications to reduce the residential energy consumption in the era of climate change.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.2
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• pp.25-39
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• 2019

The water pollution Accident in the South Han River is increasing due to increase of pollutants inflow from small streams from rural areas and reduced flow rate. This study predicted the change of water quality in the main stream of the South Han River due to climate change through the linkage of watershed and water quality models. Also, This study analyzed the effect of water quality improvement on Seomgang and the South Han River by securing the flow during the dry season. According to the scenarios for securing the river flow during drought season, the river flow in the Seomgang is increased up to 2.19 times, and the water quality during the drought season was improved up to $BOD_5$ 20.5%, T-N 40.8%, T-P 53.4%. Also, the water quality of the main stream of the South Han River improved to 5.22% of $BOD_5$, 5.42% of T-N and 7.69% of T-P as the river flow was secured from the Seomgang. The result of this study confirms that securing the baseflow in the Seomgang according to the scenarios for securing the river flow during the dry season has a positive effect on the improvement of the water quality of the rivers in the main river of the Seomgang and South Han River. The results of this study will contribute to the establishment of reasonable management to improve the water quality of the main stream of the Seomgang and South Han River.

• Atmosphere
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• v.25 no.4
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• Atmosphere
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• v.31 no.5
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.345-363
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• 2003

From the General Circulation Models(GCMs), it is known that the increases of concentrations of greenhouse gases will have significant implications for climate change in global and regional scales. The GCM has an uncertainty in analyzing the meteorologic processes at individual sites and so the 'downscaling' techniques are used to bridge the spatial and temporal resolution gaps between what, at present, climate modellers can provide and what impact assessors require. This paper describes a method for assessing local climate change impacts using a robust statistical downscaling technique. The method facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing. The construction of climate change scenarios based on spatial regression(transfer function) downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translates the GCM grid-box predictions with coarse resolution of climate change to site-specific values and the values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather values. In this study, the global climate change scenarios are constructed using the YONU GCM control run and transient experiments.

• Journal of Climate Change Research
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• v.2 no.2
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• pp.69-77
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• 2011

While increased use of energy and fossil fuel in the recent years could worsen air quality and climate change, only few studies have been conducted on estimation of greenhouse gas emissions and characterization of emission types by sectors and regions in Korea. In this study, greenhouse gases emissions based on resions(Si, Gun, Gu) and emitted sectors(industry, transport, cemmercial and institutional, residential, waste, agriculture, others) were investigated using GHG-CAPSS(Greenhouse GasClean Air Policy Support System) developed to support to national and regional greenhouse gases reduction strategies. GHG-CAPSS follows IPCC(Intergovernmental Panel on Climate Change) Guideline methodology to categorize the emission sources and estimation of greenhouse gases using bottom-up approach. Estimated total greenhouse gases emissions were 588,011 thousand tons as $CO_2$ equivalent. Industry(50.1%) sector exhibited the highest portion followed by transport(17.6%), commercial and institutional(12.6%), residential(12.6%), waste(2.6%), agriculture(2.5%). Based on regional estimation, Gyeonggi(14.9%) demonstrated the highest emitted greenhouse gases among big cities followed by Jeonnam(12.4%), Gyeongbuk(11.0%), Ulsan(9.2%) and Seoul(8.9%).

• Journal of the Korean Regional Science Association
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• v.15 no.3
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• pp.1-13
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• 1999

Since the 1990's, the most important environmental issue on the earth is characterized by "global worming problem". The United Nations Framework Convention on Climate Change (UNFCCC) plays an significant role to solve this problem on a worldwide scale. The main purpose of this paper is to analyse the impact of $CO_2$ reduction on the Daegu regional economy through 1995 regional input-output coefficients derived from the 1995 national input coefficients table by using non-survey method. The sectoral impacts on output, income, and employment were computed under the decline-unequalized assumption in final demand influenced by $CO_2$ reduction. This article has six main sections. Section 1 is an introduction to this paper. Section 2 explains briefly the derivation method of the regional technical coefficients. Section 3 describes the model building through input-output multipliers. In section 4 regional data on output, income, employment and final demand are computed to estimate the regional impacts. Section 5 deals with impact analysis on the Daegu economy. Section 6 contains a brief summary and concludintg remarks. The research findings of this study can be summarized as follows. In 1995, under the assumption of 10% decrease on an average in final demand sectors, the economy of the region studied decreased \3600 billion of output, ￦1114 billion of income, and 49919 man-years of employment. The percent ratios of each value to the total showed 9.4%, 9.7%, and 9.2%, respectively. The dominant sectors associated with impact analysis within the region are chemicals and chemical products, paper, printing and publishing, and textiles and leather, etc; nevertheless, the least dominant sector is non-metallic mineral products. products.