• Journal of Korea Water Resources Association
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• v.32 no.1
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• pp.15-29
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• 1999

A new methodology to analyze and quantify regional meteorological drought based on annual precipitation data has been introduced in this paper In this study, based on posterior probability estimator and Bayesian classifier in Spatial Analysis Neural Network (SANN), point drought probabilities categorized as extreme, severe, mild, and non drought events has been defined, and a Bayesian Drought Severity Index (BPSI) has been introduced to classify the region of interest into four drought severities. In addition, to estimate the regional drought severity for the entire region, regional extreme, severe, mild, and non drought probabilities which are the areal averages of point drought probabilities over the region has been computed and applied. In this study, the proposed methodology has been applied to analyze the regional drought of South Korea during 1967-1996 years. The drought severity for the whole South Korea was defined spatially at each year and each year was classified in a drought severity criterion. The results may be useful for water manager to understand the South Korean drought with respect to the spatial and temporal variation.

• Proceedings of the Korea Water Resources Association Conference
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• 1998.05b
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• pp.7-13
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• 1998

A methodology to analyze and quantify regional meteorological drough based on annual precipitation data has been introduced in this paper In this study, based on posterior probability estimator and Bayesian classifier in Spatial Analysis Neural Network ISANN), point drought probabilities categorized as extreme, severe, mild, and non drought events has been defined, and a Bayesian Drought Severity Index (BPSI) has been introduced to classify the region of interest into four drought serverities. For example, the proposed methodology has been applied to analyze the regional drought of South Korea. This is a new method to classify and quantify the spatial or regional drought based on neural network pattern recognition technique and the results show that it may be apprepriate and valuable to analyze the spatial drought.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.309-319
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• 2023

Recently, the frequency and intensity of meteorological disasters have increased due to climate change. In South Korea, there are regional differences in vulnerability and response capability to cope with climate change because of regional climate characteristics. In particular, drought results from various factors and is linked to extensive meteorological, hydrological, and agricultural impacts. Therefore, in order to effectively cope with drought, it is necessary to use a composite drought index that can take into account various factors, and to evaluate future droughts comprehensively considering climate change. This study evaluated hydrologic risk(${\bar{R}}$) of future drought in the Nakdong River basin based on the Dynamic Naive Bayesian Classification (DNBC)-based composite drought index, which was calculated by applying Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), Evaporate Stress Index (ESI) and Water Supply Capacity Index (WSCI) to the DNBC. The indices used in the DNBC were calculated using observation data and climate scenario data. A bivariate frequency analysis was performed for the severity and duration of the composite drought. Then using the estimated bivariate return periods, hydrologic risks of drought were calculated for observation and future periods. The overall results indicated that there were the highest risks during the future period (2021-2040) (${\bar{R}}$=0.572), and Miryang River (#2021) had the highest risk (${\bar{R}}$=0.940) on average. The hydrologic risk of the Nakdong River basin will increase highly in the near future (2021-2040). During the far future (2041-2099), the hydrologic risk decreased in the northern basins, and increased in the southern basins.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.193-193
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• 2023

여러 기후변화 시나리오에 의하면 기상재해의 발생빈도 및 강도가 증가할 것으로 예상된다. 그중 가뭄은 강수량 부족, 하천유량 감소, 토양 함수량 감소, 용수 수요량 증가 등의 다양한 요인으로 인해 발생하며, 한 가지 형태뿐만 아니라 복합적인 형태로 발생할 수 있다. 또한, 우리나라는 지역마다 기후 특성의 편차가 있어 기후변화에 따른 가뭄 취약성과 대응 능력이 지역마다 다르게 나타난다. 따라서 가뭄에 대응하기 위해서는 다양한 요인을 고려한 통합가뭄지수를 활용해야 하며, 미래의 기후변화를 고려하여 종합적으로 가뭄을 평가해야 한다. 본 연구에서는 동적 베이지안 분류기(DNBC) 기반의 통합가뭄지수를 활용하여 우리나라 전국에 대해 수문학적 위험도를 분석하고 미래 가뭄을 전망하였다. 기상학적, 수문학적, 농업적 및 사회경제적 요인을 고려한 통합가뭄지수를 산정하기 위하여 DNBC 분류기의 인자로 기후변화 시나리오 기반의 기상학적 가뭄지수 SPI, 수문학적 가뭄지수 SDI, 농업적 가뭄지수 ESI와 사회경제적 가뭄지수 WSCI를 활용하였다. 산정된 통합가뭄지수의 시계열을 기반으로 심도와 지속기간을 추출하고, 코플라 함수를 활용한 이변량 가뭄빈도분석을 수행하였다. 이후, 이변량 가뭄빈도분석에 의해 산정된 재현기간을 활용하여 수문학적 위험도를 산정하였다. 그 결과, P1(2021~2040) 기간이 수문학적 위험도 R=0.588로 가장 높은 위험도를 나타냈으며, 이후 P2(2041~2070) 기간까지 감소하였다가 P3(2071~2099) 기간에 다시 증가하는 추세를 보였다. P1(2021~2040) 기간과 P3(2071~2099) 기간은 영산강 유역이 각각 R=0.625(P1), R=0.550(P3)으로 가장 높은 위험도를 나타냈으나, P2(2041~2070) 기간은 금강 유역이 수문학적 위험도 R=0.482로 가장 높게 나타났다. 본 연구결과를 통해 향후 미래 가뭄에 대한 가뭄계획 수립 시에 기초자료로서 활용성이 높을 것으로 기대된다.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.769-777
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• 2021

This study investigated the change of the achievement rate of the target water quality conditioned on the occurrence of severe drought, to assess the effects of meteorological drought on the water quality management in the Nakdong River basin. Using three drought indices with difference time scales such as 30-, 60-, 90-day, i.e., SPI30, SPI60, SPI90, and three water quality indicators such as biochemical oxygen demand (BOD), total organic carbon (TOC), and total phosphorus (T-P), we first analyzed the relationship between severe drought occurrence water quality change in mid-sized watersheds, and identified the watersheds in which water quality was highly affected by severe drought. The Bayesian network models were constructed for the watersheds to probabilistically assess the relationship between severe drought and water quality management. Among 22 mid-sized watersheds in the Nakdong River basin, four watersheds, such as #2005, #2018, #2021, and #2022, had high environmental vulnerability to severe drought. In addition, severe drought affected spring and fall water quality in the watershed #2021, summer water quality in the #2005, and winter water quality in the #2022. The causal relationship between drought and water quality management is usufaul in proactive drought management.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.150-150
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• 2017

기후변화로 인하여 극한 홍수와 극한 가뭄 발생이 증가할 것으로 전망하고 있어 이에 대한 위험이 대두되고 있는 실정이다. 홍수 및 가뭄 수문시계열의 빈도해석시에 일반적으로 활용되는 정상성 빈도해석기법은 수문자료의 정상성을 기반으로 한 빈도해석이 대부분이기 때문에 기후변화 및 수문자료의 비정상성을 반영한 새로운 빈도해석 기법이 요구되고 있는 상황이다. 본 연구에서는 5개의 대표 관측지점(서울, 포항, 추풍령, 여수, 광주)를 선별하고 1976년부터 2015년까지 일강우자료를 활용하여 기상학적 가뭄지수인 SPI(Standardized Precipitation Index)를 산정하였다. 산정한 SPI의 경향성을 Mann-Kendall 분석을 하였으며, 정상성 및 비정상성 빈도해석을 위하여 최적확률분포로 선정된 GEV 분포 적용하였다. 본 연구에서는 가뭄빈도해석을 위하여 SPI를 입력자료로 활용하였으며, 산정된 SPI의 비정상성을 반영한 비정상성 빈도해석의 경우 Bayesian 모형을 기반으로 한 MCMC(Markov Chain Monte Carlo) 모의를 이용하여 극치분포의 사후분포 매개변수를 추정하였다. 추정 값을 바탕으로 하여 가뭄의 관측소별 빈도해석을 실시하였고 재현기간별-지속기간별 가뭄심도를 추정하여 관측소별 가뭄심도-지속기간-빈도(SDF,Severity-Duration-Frequency) 곡선을 유도하였다. 본 연구를 통하여 정상성과 비정상성 빈도해석 결과의 비교연구를 수행하였으며 기후변화에 따른 비정상 시계열로 구성된 가뭄빈도해석에 매우 유용하게 적용될 수 있을 것으로 나타났다.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.589-601
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• 2022

Although drought is a natural phenomenon, its damage occurs in combination with regional physical and social factors. Especially, related to the supply and demand of various waters, drought causes great socio-economic damage. Even meteorological droughts occur with similar severity, its impact varies depending on the regional characteristics and water supply system. Therefore, this study assessed regional drought risk considering regional socio-economic factors and water supply system. Drought hazard was assessed by grading the joint drought management index (JDMI) which represents water shortage. Drought vulnerability was assessed by weighted averaging 10 socio-economic factors using Entropy, Principal Component Analysis (PCA), and Gaussian Mixture Model (GMM). Drought response capacity that represents regional water supply factors was assessed by employing Bayesian networks. Drought risk was determined by multiplying a cubic root of the hazard, vulnerability, and response capacity. For the drought hazard meaning the possibility of failure to supply water, Goesan-gun was the highest at 0.81. For the drought vulnerability, Daejeon was most vulnerable at 0.61. Considering the regional water supply system, Sejong had the lowest drought response capacity. Finally, the drought risk was the highest in Cheongju-si. This study identified the regional drought risk and vulnerable causes of drought, which is useful in preparing drought mitigation policy considering the regional characteristics in the future.

• 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.