• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3B
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• pp.253-264
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• 2011

Drought assessment is usually performed qualitatively and/or quantitatively after defining a drought from meteorological, agricultural, hydrological, and socioeconomic perspective. Most of the drought analyses focus on meteorological, agricultural, and hydrological droughts, whereas the socioeconomic drought evaluation has been not actively performed since it needs different aspects. In this study, after defining a socioeconomic drought applicable to assess droughts in Korea, we suggested Water Excess Deficiency Index (WEDI) as an useful tool to evaluate socioeconomic droughts, based on water demand condition and water supply condition. This study verified the validity of WEDI by comparing with other drought indices (SPI, PDSI) and historical drought condition in Gyeongsang-do in 2001. The results indicated that the WEDI can be used to assess regional droughts in a socioeconomic perspective.

• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.199-214
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• 2006

The efficient management strategies of reservoirs in periods of drought event are an essential element for drought planning. A Water Supply Capacity Index (WSCI) has been developed for the evaluation and effective monitoring of the supply capacity of a reservoir during a drought event. The WSCI is a measure of the duration that a reservoir can supply a required demand under the worst drought condition. The WSCI is not only a useful standard to refer to when making decisions on reservoir operations in a period of drought, but it can be also applied for setting the drought trigger in water demand sites supplied from the reservoir. The correlation between the standardized WSCI and existing drought indices such as PDSI, SPI and SWSI has been analyzed to the applicability of WSCI.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.617-628
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• 2021

Drought is a major natural disaster that causes serious social and economic losses. Local drought forecasts can provide important information for drought preparedness. In this study, we propose a new machine learning model that predicts drought by using historical drought indices and meteorological data from 10 sites from 1981 to 2020 in the southeastern part of the Korean Peninsula, Busan-Ulsan-Gyeongnam. Using Bayesian optimization techniques, a hyper-parameter-tuned Random Forest, XGBoost, and Light GBM model were constructed to predict the evaporative demand drought index on a 6-month time scale after 1-month. The model performance was compared by constructing a single site model and a regional model, respectively. In addition, the possibility of improving the model performance was examined by constructing a fine-tuned model using data from a individual site based on the regional model.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.645-658
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• 2006

A hydrological drought index, MSWSI (Modified Surface Water Supply Index) was suggested based on SWSI. South Korea was divided into 32 regions considering the distribution of available gauge station of precipitation, dam storage, stream water level and natural groundwater level. The indices estimated in the regions represent a spatially distribution of drought. Monthly MSWSI was evaluated for the period of 1974 and 2001. The result was compared with PDSI and checked the applicability of the suggested index in our hydrologic drought situation.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1125-1132
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• 2017

In a situation where the severity and frequency of drought events getting stronger and higher, many studies related to drought forecast have been conducted to improve the drought forecast accuracy. However it is difficult to predict drought events using a single model because of nonlinear and complicated characteristics of temporal behavior of drought events. In this study, in order to overcome the shortcomings of the single model approach, we first build various single models capable to explain the relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and other independent variables such as world climate indices. Then, we developed a combined models using Stochastic Gradient Descent method among Ensemble Learnings.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.1
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• pp.22-34
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• 2007

The purpose of this study is to assess agricultural drought using modified SWSI(Surface Water Supply Index) in Ango and Anseo agricultural district. Precipitation, reservoir inflow and storage data were used for input data of modified SWSI. Precipitation data was obtained from KMA(Korea Meteorological Administration) and reservoir storage data was obtained from KARICO(Korea Agricultural & Rural Infrastructure Corporation) and reservoir inflow data was simulated by SLURP(Simple LUmped Reservoir Parametric). SWSI based agricultural drought index was evaluated for the period of 1983 and 2001. As results, for the drought periods(July-1994, June-1995, May-2001) agricultural drought indices represented extremely drought states. The result was compared with PDSI and SWSI and checked the applicability of the suggested index in our agricultural drought situation.

• Journal of Korea Water Resources Association
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• v.47 no.1
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• pp.71-82
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• 2014

The objective of this study is to project and analyze drought conditions using future climate and hydrology information over South Korea. This study used three Global Climate Models (GCMs) and three hydrological models considering the uncertainty of future scenario. Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI) and Standardized Soil moisture Index (SSI) classified as meteorological, hydrological and agricultural droughts were estimated from the precipitation, runoff and soil moisture. The Mann-Kendall test showed high increase in future drought trend during spring and winter seasons, and the drought frequency of SRI and SSI is expected higher than that of SPI. These results show the high impact of climate change on hydrological and agriculture drought compared to meteorological drought.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.22-22
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• 2018

본 연구에서는 수문학적 가뭄을 분석하기 위해 두가지 지수를 개발하여 소개하고자 한다. 첫번째는, 물수지식을 기반으로 산정된 Water Budget-based Drought Index(WBDI)로 강우와 증발산의 차이를 이용하여 산정한다. 두 번째는 에너지 수지식을 기반으로 산정된 Energy-based Water Deficit Index(EWDI)로 에너지 수지 기반의 증발산, 태양복사에너지와 토양수분 등을 이용하여 산정한다. 두가지 지수 모두 인공위성 영상 자료를 활용하였다. WBDI 산정을 위한 강수량 자료는 Tropical Rainfall Measuring Mission(TRMM)과 Global Precipitation Mission(GPM)를 활용하였으며, 증발산 자료는 Moderate Resolution Imaging Spectroradiometer (MODIS) 자료를 활용하였다. EWDI 산정에 필요한 입력자료는 모두 MODIS 자료를 활용하였다. 산정된 두 가뭄지수의 수문학적 가뭄 분석을 위해 자연유출지점인 6개 지점을 선정하여 유출량 자료와 비교하였다. 유출량 자료를 활용하여 Error matrix 기법을 적용하여 두 수문학적 가뭄지수의 우리나라에서의 적용성을 파악하였다.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1803-1818
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• 2021

As the frequency of drought increases due to climate change, it is very important to have a monitoring system that can accurately determine the situation of widespread drought. However, while ground-based meteorological data has limitations in identifying all the complex droughts in Korea, satellite remote sensing data can be effectively used to identify the spatial characteristics of drought in a wide range of regions and to detect drought. This study attempted to analyze the possibility of using remote sensing data for drought identification in South Korea. In order to monitor various aspects of drought, remote sensing and ground observation data of precipitation and potential evapotranspiration, which are major variables affecting drought, were collected. The evaluation of the applicability of remote sensing data was conducted focusing on the comparison with the observation data. First, to evaluate the applicability and accuracy of remote sensing data, the correlations with observation data were analyzed, and drought indices of various aspects were calculated using precipitation and potential evapotranspiration for meteorological drought monitoring. Then, to evaluate the drought monitoring ability of remote sensing data, the drought reproducibility of the past was confirmed using the drought index. Finally, a high-resolution drought map using remote sensing data was prepared to evaluate the possibility of using remote sensing data for actual drought in South Korea. Through the application of remote sensing data, it was judged that it would be possible to identify and understand various drought conditions occurring in all regions of South Korea, including unmeasured watersheds in the future.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.171-182
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• 2014

The objective of this study is to evaluate the utilization of bivariate joint drought index in South Korea. In order to develop the bivariate joint drought index, in this study, Clayton copula was used to estimate the joint distribution function and the calibration method was employed for parameter estimation. Precipitation and soil moisture data were selected as input data of bivariate joint drought index for period of 1977~2012. The time series analysis, ROC (Receiver Operating Characteristic) analysis, spatial analysis were used to evaluate the bivariate joint drought index with SPI (Standardized Precipitation Index) and SSI (Standardized Soil moisture Index). As a result, SPI performed better for drought onset and SSI for drought demise. On the other hand the bivariate joint drought index captured both drought onset and demise very well. The ROC score of bivariate joint drought index was higher than that of SPI and SSI, and it also reflected the local drought situations. The bivariate joint drought index overcomes the limitations of existing drought indices and is useful for drought analysis.