• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.361-372
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• 2016

Reliable long-term dam inflow prediction is necessary for efficient multi-purpose dam operation in changing climate. Since 2000s the teleconnection between global climate indices (e.g., ENSO) and local hydroclimate regimes have been widely recognized throughout the world. To date many hydrologists focus on predicting future hydrologic conditions using lag teleconnection between streamflow and climate indices. This study investigated the utility of teleconneciton for predicting dam inflow with 1-month lead time at Andong dam basin. To this end 40 global climate indices from NOAA were employed to identify potential predictors of dam inflow, areal averaged precipitation, temperature of Andong dam basin. This study compared three different approaches; 1) dam inflow prediction using SWAT model based on teleconneciton-based precipitation and temperature forecast (SWAT-Forecasted), 2) dam inflow prediction using teleconneciton between dam inflow and climate indices (CIR-Forecasted), and 3) dam inflow prediction based on the rank of current observation in the historical dam inflow (Rank-Observed). Our results demonstrated that CIR-Forecasted showed better predictability than the other approaches, except in December. This is because uncertainties attributed to temporal downscaling from monthly to daily for precipitation and temperature forecasts and hydrologic modeling using SWAT can be ignored from dam inflow forecast through CIR-Forecasted approach. This study indicates that 1-month lead dam inflow forecast based on teleconneciton could provide useful information on Andong dam operation.

• Journal of Astronomy and Space Sciences
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• v.36 no.3
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• pp.149-157
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• 2019

In this study, we investigate the associations between the solar variability and teleconnection indices, which influence atmospheric circulation and subsequently, the spatial distribution of the global pressure system. A study of the link between the Sun and a large-scale mode of climate variability, which may indirectly affect the Earth's climate and weather, is crucial because the feedbacks of solar variability to an autogenic or internal process should be considered with due care. We have calculated the normalized cross-correlations of the total sunspot area, the total sunspot number, and the solar North-South asymmetry with teleconnection indices. We have found that the Southern Oscillation Index (SOI) index is anti-correlated with both solar activity and the solar North-South asymmetry, with a ~3-year lag. This finding not only agrees with the fact that El $Ni{\tilde{n}}o$ episodes are likely to occur around the solar maximum, but also explains why tropical cyclones occurring in the solar maximum periods and in El $Ni{\tilde{n}}o$ periods appear similar. Conversely, other teleconnection indices, such as the Arctic Oscillation (AO) index, the Antarctic Oscillation (AAO) index, and the Pacific-North American (PNA) index, are weakly or only slightly correlated with solar activity, which emphasizes that response of terrestrial climate and weather to solar variability are local in space. It is also found that correlations between teleconnection indices and solar activity are as good as correlations resulting from the teleconnection indices themselves.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.515-515
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• 2022

Climate change is strongly threatening the performance of agricultural reservoirs, which are instrumental in ensuring uninterrupted water supplies for rice cultivation in Korea. In this study, various performance indices were derived and overall sustainability of the 400 agricultural reservoirs was evaluated in the context of climate change trends during 1973-2017. Rice crop evapotranspiration, irrigation water requirements, runoff generation in the upstream watershed, and volumetric evaporation losses were plugged into a water balance model to simulate the reservoir operation during the study period. Resilience, reliability, and vulnerability are the three main indicators of reservoir performance, and these were combined into a single sustainability metric to define the overall system credibility. Historical climate data analysis confirmed that the country is facing a gradual warming shift, particularly in the central and southern agricultural regions. Although annual cumulative rainfall increased over the last 45 years, uneven monthly rainfall distribution during the dry and wet seasons also exacerbated the severity and frequency of droughts/floods. For approximately 85% of the selected reservoirs, the sustainability ranged between 0.35 to 0.77, and this range narrowed sharply with time, particularly for the reservoirs located in the western and southern coast regions. The study outcomes could help in developing the acceptable ranges of the performance indices and implementing appropriate policy and technical interventions for improving the sustainability of reservoirs with unacceptable ranges of the performance indices.

• Journal of Climate Change Research
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• v.6 no.2
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• pp.61-72
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• 2015

In this study, we have investigated monthly changes in temperature extremes in South Korea for the past (1921~2010) and the future (2011~2100). We used seven stations' (Gangneung, Seoul, Incheon, Daegu, Jeonju, Busan, Mokpo) data from KMA (Korea Meteorological Administration) for the past. For the future we used the closest grid point values to observations from the RCP8.5 scenario of 1 km resolution. The Expert Team on Climate Change Detection and Indices (ETCCDI)'s climate extreme indices were employed to quantify the characteristics of temperature extremes change. Temperature extreme indices in summer have increased while those in winter have decreased in the past. The extreme indices are expected to change more rapidly in the future than in the past. The number of frost days (FD) is projected to decrease in the future, and the occurrence period will be shortened by two months at the end of the $21^{st}$ century (2071~2100) compared to the present (1981~2010). The number of hot days (HD) is projected to increase in the future, and the occurrence period is projected to lengthen by two months at the end of the $21^{st}$ century compared to the present. The annual highest temperature and its fluctuation is expected to increase. Accordingly, the heat damage is also expected to increase. The result of this study can be used as an information on damage prevention measures due to temperature extreme events.

• Journal of Climate Change Research
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• v.9 no.1
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• pp.47-58
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• 2018

In this study, we evaluated the uncertainty in the process of selecting GCM and downscaling method for assessing the impact of climate change, and influence of user-centered climate change information on reproducibility of Chungju Dam inflow was analyzed. First, we selected the top 16 GCMs through the evaluation of spatio-temporal reproducibility of 29 raw GCMs using 30-year average of 10-day precipitation without any bias-correction. The climate extreme indices including annual total precipitation and annual maximum 1-day precipitation were selected as the relevant indices to the dam inflow. The Simple Quantile Mapping (SQM) downscaling method was selected through the evaluation of reproducibility of selected indices and spatial correlation among weather stations. SWAT simulation results for the past 30 years period by considering limitations in weather input showed the satisfactory results with monthly model efficiency of 0.92. The error in average dam inflow according to selection of GCMs and downscaling method showed the bests result when 16 GCMs selected raw GCM analysi were used. It was found that selection of downscaling method rather than selection of GCM is more is important in overall uncertainties. The average inflow for the future period increased in all RCP scenarios as time goes on from near-future to far-future periods. Also, it was predicted that the inflow volume will be higher in the RCP 8.5 scenario than in the RCP 4.5 scenario in all future periods. Maximum daily inflow, which is important for flood control, showed a high changing rate more than twice as much as the average inflow amount. It is also important to understand the seasonal fluctuation of the inflow for the dam management purpose. Both average inflow and maximum inflow showed a tendency to increase mainly in July and August during near-future period while average and maximum inflows increased through the whole period of months in both mid-future and far-future periods.

• Journal of Korea Water Resources Association
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• v.47 no.12
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• pp.1107-1119
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• 2014

Interesting in abnormal climate is currently growing because of climate change. With this, an increasing number of people continue to show concern over the negative effects of such changes. In Korea, the annual average rainfall amount increased to about 19% from 1,155 mm in the 1910s to 1,375 mm in the 2000s. By the end of the 21st century, it has been projected that rainfall will further increase to about 17%. In particular, the 10-year frequency of localized heavy rain of more than 100-mm rainfall per day reached 385 days in the last 10 years. As such, it increased 1.7 times from 222 in the 1970s-80s. The extreme events caused by climate change is thus reported as having exacerbated over the years. Gangwon-province will suffer more from climate change than any other region in Korea because of its mostly mountainous terrain. It is a special region with both mountainous and oceanic climates divided alongside the eastern and western regions of the Taebaek Mountain Range. As such, this paper try to quantify using ETCCDI (Expert Team on Climate Change Detection and Indices) the recent climate changes in this region.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.97-97
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• 2021

Hydrometeorological variables contain highly complex system for temporal revolution and it is quite challenging to illustrate the system with a temporal linear and nonlinear models. In recent years, deep learning algorithms have been developed and a number of studies has focused to model the complex hydrometeorological system with deep learning models. In the current study, we investigated the temporal structure inside deep learning models for the hydrometeorological variables such as streamflow and climate indices. The results present a quite striking such that each hidden unit of the deep learning model presents different dependence structure and when the number of hidden units meet a proper boundary, it reaches the best model performance. This indicates that the deep dependence structure of deep learning models can be used to model selection or investigating whether the constructed model setup present efficient or not.

• Atmosphere
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• v.23 no.2
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• pp.237-243
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• 2013

A set of scripts for GrADS (Grid Analysis and Display System) and R was implemented to produce agricultural climate information using the future climate scenarios based on the Representative Concentration Pathways. The GrADS script was used to calculate agricultural climate indices including growing degree days and cooling degree days. The script generated agricultural climate maps of these indices, which are compatible with common Geographic Information System (GIS) applications. To perform a statistical analysis using the agricultural climate maps, a script for R, which is open source statistical software, was used. Because a large number of spatial climate data were produced, parallel processing packages such as SNOW, doSNOW, and foreach were used to perform a simple statistical analysis in the R script. The parallel script of R had speedup on workstations with multi-CPU cores.

• Journal of Climate Change Research
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• v.9 no.1
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• pp.13-29
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• 2018

This study presented evaluation procedure for selecting appropriate GCMs and downscaling method by focusing on the climate extreme indices suitable for climate change adaptation. The procedure includes six stages of processes as follows: 1) exclusion of unsuitable GCM through raw GCM analysis before bias correction; 2) calculation of the climate extreme indices and selection of downscaling method by evaluating reproducibility for the past and distortion rate for the future period; 3) selection of downscaling method based on evaluation of reproducibility of spatial correlation among weather stations; and 4) MME calculation using weight factors and evaluation of uncertainty range depending on number of GCMs. The presented procedure was applied to 60 weather stations where there are observed data for the past 30 year period on Korea Peninsula. First, 22 GCMs were selected through the evaluation of the spatio-temporal reproducibility of 29 GCMs. Between Simple Quantile Mapping (SQM) and Spatial Disaggregation Quantile Delta Mapping (SDQDM) methods, SQM was selected based on the reproducibility of 27 climate extreme indices for the past and reproducibility evaluation of spatial correlation in precipitation and temperature. Total precipitation (prcptot) and annual 1-day maximum precipitation (rx1day), which is respectively related to water supply and floods, were selected and MME-based future projections were estimated for near-future (2010-2039), the mid-future (2040-2069), and the far-future (2070-2099) based on the weight factors by GCM. The prcptot and rx1day increased as time goes farther from the near-future to the far-future and RCP 8.5 showed a higher rate of increase in both indices compared to RCP 4.5 scenario. It was also found that use of 20 GCM out of 22 explains 80% of the overall variation in all combinations of RCP scenarios and future periods. The result of this study is an example of an application in Korea Peninsula and APCC Integrated Modeling Solution (AIMS) can be utilized in various areas and fields if users want to apply the proposed procedure directly to a target area.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.174-174
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• 2023

The impact of global warming on the south Asian summer monsoon is of critical importance for the large population of this region. This study aims to investigate the future changes of the precipitation extremes during pre-monsoon and monsoon, across this region in a more organized regional structure. The study area is divided into six major divisions based on the Köppen-Geiger's climate structure and 10 sub-divisions considering the geographical locations. The future changes of extreme precipitation indices are analyzed for each zone separately using five indices from ETCCDI (Expert Team on Climate Change Detection and Indices); R10mm, Rx1day, Rx5day, R95pTOT and PRCPTOT. 10 global climate model (GCM) outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are used. The GCMs are bias corrected using nonparametric quantile transformation based on the smoothing spline method. The future period is divided into near future (2031-2065) and far future (2066-2100) and then the changes are compared based on the historical period (1980-2014). The analysis is carried out separately for pre-monsoon (March, April, May) and monsoon (June, July, August, September). The methodology used to compare the changes is probability distribution functions (PDF). Kernel density estimation is used to plot the PDFs. For this study we did not use a multi-model ensemble output and the changes in each extreme precipitation index are analyzed GCM wise. From the results it can be observed that the performance of the GCMs vary depending on the sub-zone as well as on the precipitation index. Final conclusions are made by removing the poor performing GCMs and by analyzing the overall changes in the PDFs of the remaining GCMs.