• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.471-479
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• 2018

This study analyzed the peak time of drought severity and drought period using meteorological and hydrological drought indices. Standardized Precipitation Index (SPI) using rainfall data was used for meteorological drought and Streamflow Drought Index (SDI) and Standardized Streamflow Index (SSI) using streamflow data were used for the hydrological drought. This study was applied to the Cheongmicheon watershed which is a mixture area for rural and urban regions. The rainfall data period used in this study is 32.5 years (January of 1985~June of 2017) and the corresponding streamflow was simulated using SWAT. After the drought indices were calculated using the collected data, the characteristics of drought were analyzed by time series distribution of the calculated drought indices. Based on the results of the this study, it can be seen that hydrological drought occurs after meteorological drought. The difference between SDI and SPI peak occurrence time, difference in drought start date and average drought duration is greater than SSI and SPI. In general, SSI shows more severe than SDI. Therefore, various drought indices should be used at the identification of drought characteristics.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.3
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• pp.1-14
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• 2019

North Korea has frequently suffered from extreme agricultural crop droughts, which have led to food shortages, according to the Food and Agriculture Organization (FAO). The increasing frequency of extreme droughts, due to global warming and climate change, has increased the importance of enhancing the national capacity for drought management. Historically, a meteorological drought index based on data collected from weather stations has been widely used. But it has limitations in terms of the distribution of weather stations and the spatial pattern of drought impacts. Satellite-based data can be obtained with the same accuracy and at regular intervals, and is useful for long-term change analysis and environmental monitoring and wide area access in time and space. The Evaporative Stress Index (ESI), a satellite-based drought index using the ratio of potential and actual evaporation, is being used to detect drought response as a index of the droughts occurring rapidly over short periods of time. It is more accurate and provides faster analysis of drought conditions compared to the Standardized Precipitation Index (SPI), and the Palmer Drought Severity Index (PDSI). In this study, we analyze drought events during 2015-2017 in North Korea using the ESI satellite-based drought index to determine drought response by comparing with it with the SPI and SPEI drought indices.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.795-805
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• 2013

Drought is one of the severe natural disasters and it can profoundly affect our society and ecosystem. Also, it is a very important variable for water resources planning and management. Therefore, the drought is analyzed in this study to understand the drought distribution and trend. The Standard Precipitation Index (SPI) is estimated using precipitation data obtained from 55 rain gauge stations in South Korea and the SPI based drought variables such as drought duration and drought severity were defined. Drought occurrence and joint probabilistic analysis for SPI based drought variables were performed with run theory and copula functions. And then the return period and spatial distribution of droughts on the South Korea was estimated. As the results, we have shown that Gongju and Chungju in Chungcheong-do and Wonju, Inje, Jeongseon, Taebeak in Gangwon-do have vulnerability to droughts.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.458-458
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• 2012

Climate extreme variability is a major cause of disaster such as flood and drought types occurred in Korea and its effects is also more severe damage in last decades which can be danger mature events in the future. The main aim of this study was to assess the effectives of climate change on drought for an agriculture as Nakdong basin in Korea using climate change data in the future from data of General Circulation Models (GCM) of ECHO-G, with the developing countries like Korea, the developed climate scenario of medium-high greenhouse gas emission was proposed of the SRES A2. The Standardized Precipitation Index (SPI) was applied for drought evaluation. The drought index (SPI) applied for sites in catchment and it is evaluated accordingly by current and future precipitation data, specific as determined for data from nine precipitation stations with data covering the period 1980-2009 for current and three periods 2010-2039, 2040-2069 and 2070-2099 for future; time scales of 3month were used for evaluating. The results determined drought duration, magnitude and spatial extent. The drought in catchment act intensively occurred in March, April, May and November and months of drought extreme often appeared annual in May and November; drought frequent is a non-uniform cyclic pattern in an irregular repetitive manner, but results showed drought intensity increasing in future periods. The results indicated also spatial point of view, the SPI analysis showed two of drought extents; local drought acting on one or more one of sites and entire drought as cover all of site in catchment. In addition, the meteorology drought simulation maps of spatial drought representation were carried out with GIS software to generate for some drought extreme years in study area. The method applied in this study are expected to be appropriately applicable to the evaluation of the effects of extreme hydrologic events, the results also provide useful for the drought warning and sustainable water resources management strategies and policy in agriculture basins.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.41-50
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• 2024

Precipitation is an important component of the hydrological cycle and a key input parameter for many applications in hydrology, climatology, meteorology, and weather forecasting research. Grid-based satellite rainfall products with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions. Therefore, this study aims to evaluate the commonly used new global grid-based satellite rainfall product, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), using data collected at different spatial and temporal scales. Additionally, in this study, grid-based CHIRPS satellite precipitation data were used to evaluate the 2022 extreme drought. CHIRPS provides high-resolution precipitation data at 5 km and offers reliable global data through the correction of ground-based observations. A frequency analysis was performed to determine the precipitation deficit in 2022. As a result of comparing droughts in 2015, 2017, and 2022, it was found that May 2022 had a drought frequency of more than 500 years. The 1-month SPI in May 2022 indicated a severe drought with an average value of -1.8, while the 3-month SPI showed a moderate drought with an average value of 0.6. The extreme drought experienced in South Korea in 2022 was evident in the 1-month SPI. Both CHIRPS precipitation data and observations from weather stations depicted similar trends. Based on these results, it is concluded that CHIRPS can be used as fundamental data for drought evaluation and monitoring in unmeasured areas of precipitation.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.289-289
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• 2022

최근 기후변화의 영향으로 인해 가뭄과 같은 자연재해의 발생빈도가 증가하고 있다. 가뭄은 지속 기간이 길고 정량적인 피해 규모 및 심도 파악이 어려우며, 사회, 경제적 피해와 함께 농업 시스템 전반에 심각한 영향을 줄 수 있는 재해이다. 국내 가뭄 발생 경향은 2000년 이후 급증하고 있으며, 2015년 및 2017년의 경우 이례적인 극심한 가뭄이 발생하는 등 2000년 이전과는 다른 경향을 보이고 있다. 따라서, 미래 기후변화에 따른 국내 가뭄 발생에 대비하기 위해서는 장기적인 가뭄 전망이 요구된다. CMIP6 (Coupled Model Intercomparison Project 6)에 의해 개발된 공통사회경제경로 SSP (Shared Socio-economic Pathways) 시나리오는 사회 및 경제적 요소를 내포하여 미래의 완화 및 적응 기반 기후변화 시나리오로 정의된다. 본 연구에서는 SSP 시나리오를 활용하여 미래 강수자료를 구축하여 기상학적 가뭄지수, SPI (Standaridzed Precipitation Index)를 산정하고 가뭄 특성을 분석하고자 한다. 강수자료의 경우 국내 ASOS (Automated Synoptic Observing System) 기상관측소 기준 56개소를 대상으로 1973년부터 2021년까지 49개년 자료를 수집하였으며, SSP 시나리오와 SPI를 활용하여 국내 지역을 대상으로 미래 기후변화에 따른 가뭄 전망을 수행하고자 한다. SPI는 시간척도에 따라 3개월, 6개월, 9개월, 12개월 시간척도를 적용하고, SSP 시나리오의 경우 SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5 시나리오를 적용하여 미래 기후변화 시나리오별 가뭄을 분석하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.225-225
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• 2015

Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.400-400
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• 2021

다양한 기후 연구에서는 지속적인 기후의 변화로 인한 기후 위기는 전지구적으로 아열대화와 사막화를 전망하고 있으며, 우리나라도 기후 변화로 인하여 담수 자원에 악영향을 미치고 있다. 대부분 가뭄은 기본적으로 강수량 부족에 의해 발생되며, 기상변수와 높은 상관관계를 나타내고 있다. 따라서 가뭄을 정량화하기 위한 연구들이 빈번하게 수행되며 다양한 가뭄지수들이 개발되고 있다. 기상학적 가뭄지수인 SPI(Standardized Precipitation Index)와 SPEI(Standardized Precipitation Evapotranspiration Index)는 가뭄 연구에 대표적으로 사용되는 지표이며 특히, SPEI는 강수와 증발산 사이의 물수지에 대한 평균 조건을 고려할 수 있는 것이 장점이다. 미래 가뭄 연구는 CMIP(Coupled Model Intercomparison Project)의 미래 시나리오를 이용하여 연구가 수행되고 있는데 새롭게 개발된 SSP(Shared Socioeconomic Pathways) 시나리오는 미래의 완화와 적응을 기반으로 5개의 시나리오로 구분되며, 사회 및 경제적 요소를 함께 내포하고 있어 현실적인 미래 기후를 예측할 수 있다. 과거 미래 가뭄 연구는 CMIP5의 미래 시나리오인 RCP (Representative Concentration Pathways) 시나리오를 사용한 연구가 대부분이다. 따라서 새롭게 개발된 SSP 시나리오를 이용하여 미래 가뭄 예측 연구가 필요하다. 본 연구는 SSP 시나리오의 중간 단계인 SSP2-4.5와 가장 높은 단계인 SSP5-8.5의 기후 요소를 토대로 사용하여 우리나라 미래 기간의 SPI와 SPEI를 4개(3-, 6-, 9-, 12-month)의 기간으로 구분하여 산정하였다. 시·공간적 분석을 하기 위해 가까운 미래(2025-2060)와 먼 미래(2065-2100)로 구분하였으며, 격자별로 가뭄의 심도와 발생 면적을 분석하였다. 연구 결과로 SPI의 근 미래 극한 가뭄(<-2.0)은 높았으나, 먼 미래는 오히려 전국적으로 가뭄이 줄어들었다. SPEI는 일부 지역에서 적당한 건조상태(-1.5 ~ -1.0)가 산정되었으나, 대부분 극한 가뭄이 발생하였다.

• Journal of Korea Water Resources Association
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• v.52 no.5
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• pp.325-335
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• 2019

To characterize historical droughts in the conterminous United States (CONUS), we estimated the actual evapotranspiration ($ET_a$) in the CONUS using the generalized complementary relationship (GCR) for 1895-2016. The $ET_a$ estimates were compared against simulations from the Noah land surface model (LSM). In this study, the evapotranspiration (ET) deficit defined as the difference between the wet-environment ET ($ET_w$) and $ET_a$ was then normalized to calculate the Standardized Evapotranspiration Deficit Index (SEDI) across the CONUS for the years 1895-2016. The SEDI was compared to the Standard Precipitation Index (SPI) at various time scales. The results showed that the GCR $ET_a$ was slightly higher than the Noah LSM-simualted $ET_a$. As time scales increased, the correlation between the SEDI and the SPI was higher. This study suggests that the GCR has promise as a tool in the estimation of $ET_a$ and SEDI can be useful for the drought characterization.