• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.160-160
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• 2023

Generally, Global Climate Models (GCM) cannot be used directly due to their inherent error arising from over or under-estimation of climate variables compared to the observed data. Several bias correction methods have been devised to solve this problem. Most of the traditional bias correction methods are one dimensional as they bias correct the climate variables separately. One such method is the Quantile Mapping method which builds a transfer function based on the statistical differences between the GCM and observed variables. Laux et al. introduced a copula-based method that bias corrects simulated climate data by employing not one but two different climate variables simultaneously and essentially extends the traditional one dimensional method into two dimensions. but it has some limitations. This study uses objective functions to address specifically, the limitations of Laux's methods on the Quantile Mapping method. The objective functions used were the observed rank correlation function, the observed moment function and the observed likelihood function. To illustrate the performance of this method, it is applied to ten GCMs for 20 stations in South Korea. The marginal distributions used were the Weibull, Gamma, Lognormal, Logistic and the Gumbel distributions. The tested copula family include most Archimedean copula families. Five performance metrics are used to evaluate the efficiency of this method, the Mean Square Error, Root Mean Square Error, Kolmogorov-Smirnov test, Percent Bias, Nash-Sutcliffe Efficiency and the Kullback Leibler Divergence. The results showed a significant improvement of Laux's method especially when maximizing the observed rank correlation function and when maximizing a combination of the observed rank correlation and observed moments functions for all GCMs in the validation period.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.314-314
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• 2023

Extreme climate events can have a large impact on human life by hampering social, environmental, and economic development. Global circulation models (GCMs) are the widely used numerical models to understand the anticipated future climate change. However, different GCMs can project different future climates due to structural differences, varying initial boundary conditions and assumptions about the physical phenomena. The multi-model ensemble (MME) approach can improve the uncertainties associated with the different GCM outcomes. In this study, a comprehensive rating metric was used to select the best-performing GCMs out of 11 CMIP5 and 13 CMIP6 GCMs, according to their skills in terms of four temporal and five spatial performance indices, in replicating the 21 extreme climate indices during the baseline (1975-2017) in South Korea. The MME data were derived by averaging the simulations from all selected GCMs and three top-ranked GCMs. The random forest (RF) algorithm was also used to derive the MME data from the three top-ranked GCMs. The RF-derived MME data of the three top-ranked GCMs showed the highest performance in simulating the baseline extreme climate which was subsequently used to project the future extreme climate indices under both the representative concentration pathway (RCP) and the socioeconomic concentration pathway scenarios (SSP). The extreme cold and warming indices had declining and increasing trends, respectively, and most extreme precipitation indices had increasing trends over the period 2031-2100. Compared to all scenarios, RCP8.5 showed drastic changes in future extreme climate indices. The coasts in the east, south and west had stronger warming than the rest of the country, while mountain areas in the north experienced more extreme cold. While extreme cold climatology gradually declined from north to south, extreme warming climatology continuously grew from coastal to inland and northern mountainous regions. The results showed that the socially, environmentally and agriculturally important regions of South Korea were at increased risk of facing the detrimental impacts of extreme climatology.

• Journal of the Korean earth science society
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• v.23 no.7
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• pp.552-564
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• 2002

The data of satellite-observed Microwave Sounding Unit (MSU) channel 1 (Ch1) brightness temperature and General Circulation Model (GCM) reanalyses over the globe have been used to investigate low tropospheric hydrometeors and microwave surface emissivity during the period from January 1981 to December 1993. The average of GCM Ch1 temperature has been reconstructed from three kinds of reanalyses, based on the MSU weighting function. Since the GCM temperature mainly corresponds to the thermal state of the lower troposphere without the difference in the emissivity between ocean and land, it is higher in summer than in other seasons over the regions. The MSU temperature over the ocean shows its maximum at the ITCZ and the SPCZ due to hydrometeors. Over high latitude ocean, the temperature is enhanced because of sea ice emissivity, while it is reduced over the land. The seasonal displacement of the ITCZ and the SPCZ systematically appeared in the difference of Ch1 temperature between the GCM and the MSU. The difference values decrease in the regions of the ITCZ, the SPCZ, and the sea ice because of the increase of the MSU temperature. According to the local minima of the values, the ITCZ moves norhward to 9 N in fall, and the SPCZ moves southward to 12 S in boreal fall and winter. The sea ice in the northern hemisphere is extended southward to 53 N in winter, while the ice in the southern hemisphere, northward to 58 S in boreal summer. We also have discussed the separated contribution from hydrometeors and surface emissivity to the MSU Ch1 temperature, utilizing radiative transfer theory. The increase of 4-6K in the temperature over the ITCZ is inferred to result from hydrometeors of 1-1.5mm/day, and furthermore the increase of 10-30K over the high latitude ocean, ice emissivity of 0.6-0.9.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.807-819
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• 2013

This study proposes a new methodology for future water balance projection considering climate change by assigning a weight to each scenario instead of inputting future streamflows based on GCMs into a water balance model directly. K-nearest neighbor algorithm was employed to assign weights and streamflows in non-flood period (October to the following June) was selected as the criterion for assigning weights. GCM-driven precipitation was input to TANK model to simulate future streamflow scenarios and Quantile Mapping was applied to correct bias between GCM hindcast and historical data. Based on these bias-corrected streamflows, different weights were assigned to each streamflow scenarios to calculate water shortage for the projection periods; 2020s (2010~2039), 2050s (2040~2069), and 2080s (2070~2099). As a result by applying the proposed methodology to project water shortage over the Korean Peninsula, average water shortage for 2020s is projected to increase to 10~32% comparing to the basis (1967~2003). In addition, according to getting decreased in streamflows in non-flood period gradually by 2080s, average water shortage for 2080s is projected to increase up to 97% (516.5 million $m^3/yr$) as maximum comparing to the basis. While the existing research on climate change gives radical increase in future water shortage, the results projected by the weighting method shows conservative change. This study has significance in the applicability of water balance projection regarding climate change, keeping the existing framework of national water resources planning and this lessens the confusion for decision-makers in water sectors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.525-533
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• 2008

Global circulation models (GCMs) have been used to study impact of climate change on water resources for hydrologic models as inputs. Recently, regional circulation models (RCMs) have been used widely for climate change study, but the RCMs have been rarely used in the climate change impacts on water resources in Korea. Therefore, this study is intended to use a set of climate scenarios derived by RegCM3 RCM ($27km{\times}27km$), which is operated by Korea Meteorological Administration. To begin with, the RCM precipitation data surrounding major rainfall stations are extracted to assess validation of the scenarios in terms of reproducing low frequency behavior. A comprehensive comparison between observation and precipitation scenario is performed through statistical analysis, wavelet transform analysis and EOF analysis. Overall analysis confirmed that the precipitation data driven by RegCM3 shows capabilities in simulating hydrological low frequency behavior and reproducing spatio-temporal patterns. However, it is found that spatio-temporal patterns are slightly biased and amplitudes (variances) from the RCMs precipitation tend to be lower than the observations. Therefore, a bias correction scheme to correct the systematic bias needs to be considered in case the RCMs are applied to water resources assessment under climate change.

• Korean Journal of Crystallography
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• v.17 no.1
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• pp.10-13
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• 2006

The crystal structure of Ethylenediammonium chromate, $C_2H_{10}N_2{\cdot}CrO_4$, has been determined by X-ray crystallography. Crystal data: a=6.667(2), b=8.845(2), c:11.827(2) ${\AA}$, Orthorhombic, $P2_12_12_1$(Space Group No=19), Z=4, V=697.4(3) ${\AA}{^3},\;Dc=1.696gcm^{-3},\;{\mu}=1.594mm^{-1}$. The structure was solved by Patterson method and refined by full matrix least-square methods using unit weights. The final R and S values were $R_1=0.0254,\;R_w=0.070,\;R_{all}=0.0255$ and S=1.133 for the observed 1195 reflections. Bond length and angles of two ions are similar to the previously reported data. The ethylenediammonium ion has trans-configuration and are linked through many hydrogen bonds with neighboring anions.

• Korean Journal of Crystallography
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• v.15 no.2
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• pp.74-77
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• 2004

The crystal structure of Dinitroethylenediaminepalladium(II), $Pd(C_2H_8N_2)(NO_2)_2$, has been determined by X-ray crystallography. Crystal data: a=7.425(3), b=8.480(4), c=11.885(2) ${\AA}$, Orthorhombic, $A2_1ma$ (Space Group No=36), Z=4, V=748.3(4) ${\AA}^3,\;D_c=2.295 gcm^{-3},\;{\mu}=2.457mm^{-1}$. The structure was solved by Patterson method and refined by full matrix least-square methods using unit weights. The final R and S values were $R_1=0.0306,\;R_w=0.0802,\;R_{all}=0.0320,\;and\;S=1.166)$ for the observed 377 reflections. Bond lengths and angles of palladium complex are similar to the previously reported data. The complex structure is one dimensional Reiset's salt type analogue showing zigzag chain of Pd-Pd length and angle of 3.762(2) ${\AA}$ and $161.41(5)^{\circ}$. The complex molecules are linked through inter-and intramolecular hydrogen bonds of 3.05(1) and 3.15(1) ${\AA}$ between oxygen and nitrogen.

• Korean Journal of Crystallography
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• v.9 no.1
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• pp.71-76
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• 1998

Crystal structure of Bis(ethylenediamine)palladium(II)-Bis(oxalato)palladate(II0 has been determined by X-ray crystallography. Crystal data : (Pd(C2H8N2)2.Pd(C2O4)2), Fw=509.04, Monocline, Space Group P21/c (no=14), a=6.959(2), b=13.506(2), c=15.339(2) Å, β=99.94(3), Z=4, V=1420 Å3, Dc=2.380 gcm-3, μ=25.46cm-1, F(000)=992. The intensity data were collected with Mo-Kα radiation (λ=0.7107 Å) on an automatic four-circle diffractometer with a graphite monochromater. The structure was solved by Patterson method and refined by full matrix least-square methods using unit weights. The final R and S values were R=0.021, Rw=0.030, Rall=0.032 abd S=2.1 for 1472 observed reflections. The essentially planar complex anions form diade of interplanar distances of 3.41 Å and their diads are stacked along aaxis with interplanar separation of 3.44 Å.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.2
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• pp.35-52
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• 2024

This study conducted an assessment of potential impacts on the drought in agricultural reservoirs using the recently proposed SSP (Shared Socioeconomic Pathways) scenarios by IPCC (Intergovernmental Panel on Climate Change). This study assesses the potential impact of climate change on agricultural water resources and infrastructure vulnerability within Gyeongsangnam-do, focusing on 15 agricultural reservoirs. The assessment was based on the KRC (Korea Rural Community Corporation) 1^st vulnerability assessment methodology using RCP scenarios for 2021. However, there are limitations due to the necessity for climate impact assessments based on the latest climate information and the uncertainties associated with using a single scenario from national standard scenarios. Therefore, we applied the 13 GCM (General Circulation Model) outputs based on the newly introduced SSP scenarios. Furthermore, due to difficulties in data acquisiton, we reassessed potential impacts by redistributing weights for proxy variables. As a main result, with lower future potential impacts observed in areas with higher precipitation along the southern coast. Overall, the potential impacts increased for all reservoirs as we moved into the future, maintaining their relative rankings, yet showing no significant variability in the far future. Although the overall pattern of potential impacts aligns with previous evaluations, reevaluation under similar conditions with different spatial resolutions emphasizes the critical role of meteorological data spatial resolution in assessments. The results of this study are expected to improve the credibility and accuracy formulation of vulnerability employing more scientific predictions.

• 한국태양에너지학회:학술대회논문집
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• 2011.04a
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• pp.54-58
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• 2011

According to the Fourth Assessment Report of the Inter governmental Panel on Climate Change(IPCC), climate change is already in progress around the world, and it is necessary to start mitigation and adaptation strategies for buildings in order to minimize adverse impacts. It is likely that the South Korea will experience milder winters and hotter and more extreme summers. Those changes will impact on building performance, particularly with regard to cooling and ventilation, with implications for the quality of the indoor environment, energy consumption and carbon emissions. This study generate weather data for future climate change for use in impacts studies using PRECIS (Providing REgional Climate for Impacts Studies). These scenarios and RCM (Regional Climate Model) are provided high-resolution climate-change predictions for a region generally consistent with the continental-scale climate changes predicted in the GCM (Global Climate Model).