• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.299-303
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• 2008

In this study, an uncertainty assessment for surface air temperature(T2m) and precipitation(PCP) over East Asia is carried out. The data simulated by the intergovermental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Atmosphere-Ocean coupled general circulation Model (AOGCM) are used to assess the uncertainty. Examination of the seasonal uncertainty of T2m and PCP variabilities shows that spring-summer cold bias and fall warm bias of T2m are found over both East Asia and the Korea peninsula. In contrast, distinctly summer dry bias and winter-spring wet bias of PCP over the Korea peninsula is found. To investigate the PCP seasonal variability over East Asia, the cyclostationary empirical orthogonal function(CSEOF) analysis is employed. The CSEOF analysis can extract physical modes (spatio-temporal patterns) and their undulation (PC time series) of PCP, showing the evolution of PCP. A comparison between spatio-temporal patterns of observed and modeled PCP anomalies shows that positive PCP anomalies located in northeastern China (north of Korea) of the multi-model ensemble(MME) cannot explain properly the contribution to summer monsoon rainfalls across Korea and Japan. The uncertainty of modeled PCP indicates that there is disagreement between observed and MME anomalies. The spatio-temporal deviation of the PCP is significantly associated with lower- and upper-level circulations. In particular, lower-level moisture transports from the warm pool of the western Pacific and corresponding moisture convergence significantly contribute to summer rainfalls. These lower- and upper-level circulations physically consistent with PCP give a insight of the reason why differences between modeled and observed PCP occur.

• 대기
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• 제30권3호
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• pp.293-309
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• 2020

There are lots of indices that define the intensity of East Asian summer monsoon (EASM) in climate systems. This paper assesses the prediction skill for EASM indices in a Global Seasonal Forecasting System (GloSea5) that is currently operating at KMA. Total 5 different types of EASM indices (WNPMI, EAMI, WYI, GUOI, and SAHI) are selected to investigate how well GloSea5 reproduces them using hindcasts with 12 ensemble members with 1~3 lead months. Each index from GloSea5 is compared to that from ERA-Interim. Hindcast results for the period 1991~2010 show the highest prediction skill for WNPMI which is defined as the difference between the zonal winds at 850 hPa over East China Sea and South China Sea. WYI, defined as the difference between the zonal winds of upper and lower level over the Indian Ocean far from East Asia, is comparatively well captured by GloSea5. Though the prediction skill for EAMI which is defined by using meridional winds over areas of East Asia and Korea directly affected by EASM is comparatively low, it seems that EAMI is useful for predicting the variability of precipitation by EASM over East Asia. The regressed atmospheric fields with EASM index and the correlation with precipitation also show that GloSea5 best predicts the synoptic environment of East Asia for WNPMI among 5 EASM indices. Note that the result in this study is limited to interpret only for GloSea5 since the prediction skill for EASM index depends greatly on climate forecast model systems.

• 한국수자원학회논문집
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• 제39권4호
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• pp.335-346
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• 2006

수문모형의 많은 입력자료 가운데 강우 관측자료가 모의결과에 가장 주요한 원인을 가진다. 과거부터 수문모형은 유역에서의 평균강우량 자료를 한 지점에서의 강우량 자료에 의존해 왔다. 그러나, 지점에서 측정된 강우량 자료를 유역을 대표하는 평균강우량 산정에 이용할 경우 부적절한 경우가 발생할 수 있으며, 특히 그 계측망이 조밀하지 못할 경우 더욱 그러하다. 레이더의 경우 보다 넓은 유역 전체를 관측하기 때문에 유역에서의 평균강우량을 제공함에 있어 좋은 관측장비라 할 수 있다. 레이더를 이용하여 강우를 관측할 경우 직접적인 강우관측이 아니기 때문에 QPE에서 몇몇 한계가 있을 수 있으나, 강우자료의 공간변화도에 대한 좋은 정보를 제공해 준다. 또한 지상의 강우관측소에서의 강우측정자료는 지점에서의 강우량에 대한 정확한 정보를 제공해 준다. 따라서, 본 연구의 목적은 지상의 강우관측소에서 관측된 강우량 자료와 레이더를 이용하여 관측된 자료, 즉 두 가지 관측자료를 Ordinary cokriging 보간을 이용하여 통합함으로써 개선된 강우량 추정방법을 개발한다.

• 농촌계획
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• 제24권4호
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• pp.99-119
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• 2018

Unit load factor, which is used for the quantification of non-point pollution in watersheds, has the limitation that it does not reflect spatial characteristics of soil, topography and temporal change due to the interannual or seasonal variability of precipitation. Therefore, we developed the method to estimate a watershed-scale non-point pollutant load using seasonal forecast data that forecast changes of precipitation up to 6 months from present time for watershed-scale water quality management. To establish a preemptive countermeasure against non-point pollution sources, it is possible to consider the unstructured management plan which is possible over several months timescale. Notably, it is possible to apply various management methods such as control of sowing and irrigation timing, control of irrigation through water management, and control of fertilizer through fertilization management. In this study, APEX-Paddy model, which can consider the farming method in field scale, was applied to evaluate the applicability of seasonal forecast data. It was confirmed that the rainfall amount during the growing season is an essential factor in the non-point pollution pollutant load. The APEX-Paddy model for quantifying non-point pollution according to various farming methods in paddy fields simulated similarly the annual variation tendency of TN and TP pollutant loads in rice paddies but showed a tendency to underestimate load quantitatively.

• 한국수자원학회논문집
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• 제51권spc1호
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• pp.1045-1056
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• 2018

지난 수십 년 동안, 변화하는 기후를 이해하기 위해 많은 연구가 있었고, 여러 기후 변량의 변화추세가 발견되었다. 이들 추세를 설명하기는 쉽지 않고, 때로는 서로 모순적으로 보여지기도 한다. 다양한 기후 변량의 변화추세를 일반적으로 설명하기 위한 가설을 제시하기 위해 많은 시도도 있었다. 어느 정도 지식이 축적된 이 시점에서, 우리 시대에 진행되는 기후 변화에 대해 우리가 알고 있는 것을 되돌아보고 신중히 정리해볼 필요가 있다. 여기서는 기온, 일사량, 풍속, 증발, 강수에 초점을 맞추어 변화하는 기후에 대해 우리가 얻은 지식을 종합적으로 살펴보고자 한다. 온난화, 흐려짐, 잔잔하기로 대변되는 주요 변화추세 및 증발의 역설, 강수 변동성의 증가를 서울 지점 자료를 예시로 들어 설명한다. 이러한 변화에 대한 이해를 근거로 수문학자와 공학자들에게 네 가지 함의를 제시하려 한다.

• 한국환경과학회지
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• 제28권2호
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• pp.169-182
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• 2019

Temporal and spatial variations in surface ozone concentrations in Busan were investigated by using observation data from urban air quality sites during 2001-2016. The annual ozone concentrations showed a significant increasing trend of $+0.40ppb\;yr^{-1}$ in this period, with a more rapid increase of $+0.81ppb\;yr-1$ since 2010. For the monthly analysis, the increase in ozone concentration was the greatest in August ($+0.68ppb\;yr-1$). These ozone trends were due mainly to rising temperature ($+0.05^{\circ}C\;yr^{-1}$) and weak decreasing precipitation ($-6.42mm\;yr^{-1}$). However, the extreme weather events (heat wave, localized heavy rain, etc.) lead to an increase in short-term variability of ozone since 2010. The relatively low ozone concentrations in the downtown area were caused by high NOx emissions from mobile sources. The increases in ozone concentrations were observed at most of the air quality monitoring sites due to the reductions in anthropogenic emissions of NOx during 2001-2015. However, in the southern coastal area, lower rates of increase in ozone concentrations were observed by $-0.10{\sim}0.25ppb\;yr^{-1}$ due to the significant NOx emitted by ships in the Busan port and Busan new port.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.178-178
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• 2023

Drought and flooding have historically coexisted in Korea, occurring at different times and with varying cycles and trends. The drought indicators measured were (PDSI), (SPI), and (SPEI) in order to statistically analyze the annual or periodic drought occurrence and objectively evaluate statistical characteristics such as the periodicity, tendency, and frequency of occurrence of droughts in the Doam watershed. To compute potential evapotranspiration (PET), both Thornthwaite (Thor) and Penman-Monteith (PM) parameterizations were considered, and the differences between the two PET estimators were analyzed. Hence, SPIs 3 and SPIs 6 revealed a tendency to worsen drought in the spring and winter and a tendency to alleviate drought in the summer in the study area. The seasonal variability trend did not occur in the SPIs 12 and PDSI, as it did in the drought index over a short period. As a result of the drought trend study, the drought from winter to spring gets more severe, in addition to the duration of the drought, although the periodicity of the recurrence of the drought ranged from 3 years to 6 years at the longest, indicating that SPIs 3 showed a brief time of around 1 year. SPIs 6 and SPIs 12 had a term of 4 to 6 years, and PDSI had a period of roughly 6 years. Based on the indicators of the PDSI, SPI, and SPEI, the drought severity increases under climate change conditions with the decrease in precipitation and increased water demand as a consequence of the temperature increase. Therefore, our findings show that national and practical measures are needed for both winter and spring droughts, which happen every year, as well as large-scale and extreme droughts, which happen every six years.

• 한국농공학회논문집
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• 제66권4호
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• pp.1-15
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• 2024

In recent years, climate change has been responsible for unusual weather patterns on a global scale. Droughts, natural disasters triggered by insufficient rainfall, can inflict significant social and economic consequences on the entire agricultural sector due to their widespread occurrence and the challenge in accurately predicting their onset. The frequency of drought occurrences in South Korea has been rapidly increasing since 2000, with notably severe droughts hitting regions such as Incheon, Gyeonggi, Gangwon, Chungbuk, and Gyeongbuk in 2015, resulting in significant agricultural and social damage. To prepare for future drought occurrences resulting from climate change, it is essential to develop long-term drought predictions and implement corresponding measures for areas prone to drought. The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report outlines a climate change scenario under the Shared Socioeconomic Pathways (SSPs), which integrates projected future socio-economic changes and climate change mitigation efforts derived from the Coupled Model Intercomparison Project 6 (CMIP6). SSPs encompass a range of factors including demographics, economic development, ecosystems, institutions, technological advancements, and policy frameworks. In this study, various drought indices were calculated using SSP scenarios derived from 18 CMIP6 global climate models. The SSP5-8.5 scenario was employed as the climate change scenario, and meteorological drought indices such as the Standardized Precipitation Index (SPI), Self-Calibrating Effective Drought Index (scEDI), and Standardized Precipitation Evapotranspiration Index (SPEI) were utilized to analyze the prediction and variability of future drought occurrences in South Korea.

• 한국기후변화학회지
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• 제34권13호
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• pp.5243-5256
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• 2021

The North Pacific Oscillation (NPO), a primary atmospheric mode over the North Pacific Ocean in boreal winter, is known to trigger El Niño-Southern Oscillation (ENSO) in the following winter, the process of which is recognized as the seasonal footprinting mechanism (SFM). On the basis of the analysis of model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), we found that the SFM acts differently among models, and the correlation between the NPO and subsequent ENSO events, called the SFM efficiency, depends on the background mean state of the model. That is, SFM efficiency becomes stronger as the climatological position of the Pacific intertropical convergence zone (ITCZ) moves poleward, representing an intensification of the northern branch of the ITCZ. When the Pacific ITCZ is located poleward, the wind-evaporation-sea surface temperature (SST) feedback becomes stronger as the precipitation response to the SST anomaly is stronger in higher latitudes than that in lower latitudes. In addition, such active ocean-atmosphere interactions enhance NPO variability, favoring the SFM to operate efficiently and trigger an ENSO event. Consistent with the model results, the observed SFM efficiency increased during the decades in which the northern branch of the climatological ITCZ was intensified, supporting the importance of the tropical mean state of precipitation around the Pacific ITCZ.

• 대한토목학회논문집
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• 제43권1호
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• pp.43-54
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• 2023

기후변화가 고착화되면서 강수와 기온 변동으로 인한 가뭄 및 홍수 발생이 증가하고 있다. 유역 단위의 유출량 예측은 기후변화로 인한 자연재해에 대비하기 위한 수자원 관리의 시작이라 할 수 있다. 하지만, 기후변화와 유출모형의 불확실성은 정확한 유출 분석을 어렵게 한다. 본 연구에서는 이러한 불확실성을 완화하기 위하여 기후 스트레스 시나리오에 따른 두 개의 집중형 수문모형, 즉 IHACRES와 GR4J를 이용하여 강수 및 기온 변화에 따른 유출량 변화를 비교, 분석하였다. 연구 대상 지역은 합천댐과 섬진강댐 유역이며, Nash-Sutcliffe Efficiency (NSE) 및 Kling Gupta Efficiency (KGE)를 목적함수로 하여 각 모형의 매개변수를 최적화하였다. 모형의 보정과 검정은 20년(1995년-2014년)의 유출자료를 활용하였으며, 보정 및 검정 기간은 각각 7:3 비율로 설정하였다. 두 모형 모두 보정과 검정 기간에 비교적 높은 신뢰도(NSE>0.74, KGE>0.75)를 보여, 모형이 과거 사상을 재현하기에 적합하고, 모의 결과가 비교적 유사함을 확인하였다. 다음으로, 기후변동이 유출에 미치는 영향을 평가하기 위해 동일한 모의 기간에 대해 강수는 -50 %에서 +50 %의 범위를 1 %씩, 기온은 0 ℃에서 8 ℃까지 0.1 ℃씩 구분하여 총 8,181개의 기후조건 시나리오를 구축하였다. 이후, 기후 스트레스 시나리오에 따른 두 모형의 최대유량, 풍수량, 평수량을 비교 및 분석하였다. 기후 스트레스 영향을 반영한 연최대유량과 풍수량의 경우, 강수 감소에 따른 유출 패턴은 두 모형에서 비슷하였으나, 강수와 기온이 증가할수록 상이한 결과를 얻었다. 이와 반대로, 풍수량의 경우 강수와 기온 변화의 차이가 커질수록 두 모형은 유사한 결과를 얻었다. 즉, 유역의 탄력적 기후변화 대응을 위해서는 모형의 불확실성에 대한 정량적 평가가 필요하다는 것을 시사한다.