Drought In South Korea Research Articles

Forecasting Meteorological Drought Conditions in South Korea Using a Data-Driven Model with Lagged Global Climate Variability

Drought prediction is crucial for early risk assessment, preventing negative impacts and the timely implementation of mitigation measures for sustainable water management. This study investigated the relationship between climate variations in three seas and the prediction of December meteorological droughts in South Korea, using the Standardized Precipitation Evapotranspiration Index (SPEI). Climate indices with multiple time lags were integrated into multiple linear regression (MLR) and Random Forest (RF) models and evaluated using Pearson’s correlation coefficients (PCCs) and the Root Mean Square Error (RMSE). The results indicated that the MLR model outperformed RF model in the western inland region with a PCC of 0.52 for predicting SPEI-2. On the other hand, the RF model effectively predicted drought states of ‘moderate drought’ or worse (SPEI < −1) nationwide, achieving an average hit rate of 47.17% and Heidke skill score (HSS) of 0.56, particularly excelling in coastal areas. Nino 3.4 turned out to be the most influential factor for short-period extreme droughts (SPEI-2) with a three-month lag, contributed by the Pacific, Atlantic, and Indian Oceans. For periods of four months or longer, climate variations had a lower predictive value. However, integrating autocorrelation functions to account for the previous month’s drought status improved the accuracy. A HYBRID model, which blends linear and nonlinear approaches, further enhanced reliability, making the proposed model more applicable for drought forecasting in neighboring countries and valuable for South Korea’s drought monitoring system to support sustainable water management.

Characterization of groundwater drought and understanding of climatic impact on groundwater resources in Korea

Although South Korea is frequently subjected to monsoons, the shortage of summer monsoons and relatively dry spring and winter seasons cause droughts in the Korean Peninsula. Since the late 1960s, severe droughts in South Korea have encouraged extensive groundwater development for agricultural and economic growth. Therefore, identifying and estimating the impact of droughts on the country’s groundwater resources is crucial for establishing policies and regulations on groundwater exploitation and recharge. Current drought management of the country relies on a meteorological drought index. However, these estimates lack a comprehensive understanding of absolute drought severity and propagation. This study aims to develop a quantitative method for calculating the frequency and severity of groundwater droughts based on the Groundwater Deficit (GWD) derived from NASA's Gravity Recovery and Climate Experiment (GRACE) mission data. The estimated annual GWD in mass over the country ranges from -0.07 km3 to 0.04 km3, with a trend in the mass of -1.60×10−3 km3/year from 2002 to 2021. Thirteen groundwater drought events were identified based on GWD. Among those, the highest drought severities recorded in 2015 and 2019 were -34.27 km3 months and -55.38 km3 months, respectively. Monthly deficits define the quantity of water required to restore normal groundwater storage conditions. Hence, the minimum and average days to recover from groundwater droughts were estimated based on the strength of the storage-based approaches. The GWD approach aligns with meteorological drought databases with a time lag. It illustrates the transition from meteorological drought to groundwater drought, along with their onset, cessation, propagation, instantaneous severity, and peak magnitude.

우리나라에서 최근(1982 ~ 2020) 발생한 여름철 급성가뭄의 특성 및 변화에 대한 연구

A flash drought is a rapidly evolving drought, unlike general meteorological droughts that develop over months to years due to persistent precipitation deficits. This study investigated the characteristics of summertime flash droughts in South Korea for the period of 1982 ~ 2020. Flash droughts can be classified into heatwave flash droughts and precipitation deficit flash droughts, with the former occurring most frequently in South Korea. Both the occurrence and duration of flash droughts increased during the analysis period, especially heatwave flash droughts. Composite analysis results of reanalysis dataset and land surface model simulation revealed that large-scale anticyclonic atmospheric circulation and positive temperature anomalies contribute to increased evapotranspiration and reduced soil moisture, which lead to flash droughts in South Korea. In the summer of 2016 and 2018, when flash droughts occurred most frequently with record-breaking heatwaves, these characteristics were prevalent. Land-atmosphere interactions associated with the anticyclonic circulation might influence the development of summer heatwave flash droughts in South Korea.

Development of a Multiple-Drought Index for Comprehensive Drought Risk Assessment Using a Dynamic Naive Bayesian Classifier

Korea has made various efforts to reduce drought damage; however, socio-economic damage has increased in recent years due to climate change, which has led to increasing frequency and intensity of drought. In South Korea, because precipitation is concentrated in the summer, drought damage will be significant in the event of failure of water resources management. Seasonal and regional imbalances in precipitation have contributed to recent extreme droughts in South Korea. In addition, population growth and urbanization have led to increased water use and contributed to water shortage. Drought risk analysis must address multiple contributing factors and comprehensively assess the effects or occurrence of future droughts, which are essential for planning drought mitigation to cope with actual droughts. Drought mitigation depends on the water supply capacity during dry spells. In this study, a dynamic naive Bayesian classifier-based multiple drought index (DNBC-MDI) was developed by combining the strengths of conventional drought indices and water supply capacity. The DNBC-MDI was applied to a bivariate drought frequency analysis to evaluate hydrologic risk of extreme droughts. In addition, future changes of the risk were investigated according to climate change scenarios. As a result, the drought risk had a decreasing trend from the historic period of 1974–2016 to the future period of 2017–2070, then had an increasing trend in the period of 2071–2099.

Evaluation of agricultural drought in South Korea using socio-economic drought information

The effect of drought on agriculture is a complicated result of several factors. Several studies have been conducted to evaluate agricultural drought using facility monitoring or statistical data. However, the gap in awareness between the public monitoring of drought and local situations has not been sufficiently reduced. The objectives of this study were to analyze the correlation between a socioeconomic drought information (SEDI) based on Internet news articles and agricultural drought data and to determine whether the SEDI can provide reliable information regarding damage due to agricultural drought. The SEDI was subdivided into the following aspects: water deficit, water security and support, economic damage and impact, and environmental and sanitation impact. The correlation coefficient between SEDI and eight agricultural statistical datasets ranged from 0.74 to 0.89. In moderately dry irrigation and non-irrigation periods, the relationship between SEDI and the agricultural reservoir drought index (RDI) was evidenced by a receiver operating characteristic and an area under the curve of 0.60 and 0.68, respectively. The cross-correlation values between the SEDI and RDI ranged from −0.48 to −0.42. According to cross-correlation analysis, SEDI was able to detect drought one month earlier than the RDI was able to do. Thus, our proposed method can contribute to the development of an impact-based early warning system for agricultural drought management.

Spatiotemporal Rainfall Variability and Drought Assessment during Past Five Decades in South Korea Using SPI and SPEI

About 41% of the earth is drought-affected, which has impacted nearly 2 billion people, and it is expected that more than 90% of terrestrial areas will be degraded by 2050. To evade and mitigate the harmful impacts of drought, it is necessary to study the rainfall variability and assess the drought trend at a global and regional level. This study utilized 70 meteorological stations in South Korea to evaluate the rainfall variability, drought, and its trend during the past five decades using the standardized precipitation evapotranspiration index (SPEI) and the standardized precipitation index (SPI). Rainfall data normality was assessed with mean, standard deviation, skewness, and kurtosis. The highest amount of rainfall was observed in the months of June, July, and August. The SPI and SPEI 12-month results revealed that 1982, 1988, 2008, 2015, and 2017 were dry years throughout the country, while from 2013 to 2017 mixed drought events were observed for the 6-month time series. The Mann-Kendall trend test was applied to the 1- and 12-month time series, and the results revealed that the months of January, March, April, May, June, and August had a significant negative trend, which means drought is increasing in these months, while the months of September, October, and December had a significant positive trend, which means wetter conditions prevailed in these months during the study period. It was observed in the 12-month time series that only two met stations had a significant negative trend, while only one had a significant positive trend. It was found that January and March were the driest months, and October was the wettest month. The detected drought events in this research are consistent with ENSO events. We have observed differences in drought characteristics (duration and frequency) for both indices. Climatic data revealed that South Korea has faced drought conditions (rainfall deficit) due to a shortened monsoon season. This study can provide guidance on water management strategies under the changing pattern of drought in South Korea.

Spatial and Temporal Analysis of Hcho Response to Drought in South Korea

Spatial and Temporal Analysis of Hcho Response to Drought in South Korea

Differences in multi‐model ensembles of CMIP5 and CMIP6 projections for future droughts in South Korea

AbstractThis study quantified the uncertainties of future drought projections in the South Korea case by means of the reliability ensemble averaging (REA) of 10 GCM equivalents of the Coupled Model Intercomparison Project, Phases 5 and 6 (CMIP5 and CMIP6). Two meteorological drought indices, the Standardized Precipitation Index (SPI) and the Standardized Precipitation‐Evapotranspiration Index (SPEI) for Representative Concentration Pathway (RCP) 4.5 and RCP8.5 of CMIP5 and Shared Socioeconomic Pathway (SSP) 2‐4.5 and SSP5‐8.5 of CMIP6 were considered. The GCMs' performances for the historical period were evaluated, and their biases were corrected using quantile mapping. The multi‐model ensemble (MME) means of the GCMs were generated using the entropy and TOPSIS methods. The results showed that the TOPSIS MME estimated more intense droughts than the entropy MME. The levels of SPI and SPEI severity estimated using the entropy MME were higher than those from the TOPSIS MME. The SPI and SPEI outcomes of RCP4.5 were much more robust than those of SSP2‐4.5. The projected drought severity in the near future was much greater than in the far future, while the reliability of drought projections in the far future was much higher than in the near future. The reliability levels of drought projections for the SSP scenarios were higher than for the RCPs for most durations. This study can support planning and management of future droughts considering uncertainty variables.

Assessment of Vulnerability to Drought Disaster in Agricultural Reservoirs in South Korea

Drought is a natural disaster affecting agriculture worldwide. Drought mitigation and proactive response require a comprehensive vulnerability mapping approach considering various factors. This study investigates the vulnerability to agricultural drought in South Korea based on exposure, sensitivity, and adaptability. The evaluation of agricultural drought factors yielded 14 items, which are categorized into meteorological, agricultural reservoir, social, and adaptability factors. Each item is assigned a weight using the analytical hierarchy process (AHP). We analyzed vulnerability to drought disaster in agricultural reservoirs, and generated vulnerability maps by applying the vulnerability framework for climate change. The generated map was divided into four categories based on drought vulnerability: A (Very high), B (High), C (Moderate), and D (Low). The weights for the meteorological (0.498), agricultural reservoir (0.286), social (0.166), and adaptability (0.05) factors were obtained using AHP. The rating frequencies were 41.91%, 19.76%, 9.58%, and 5.39% for A, B, C, and D, respectively. The western region is extremely vulnerable to meteorological and agricultural reservoir factors, whereas the eastern region is more vulnerable to adaptability. The results of this study visually represent agricultural drought and can be used for evaluating regional drought vulnerability for assisting preemptive drought responses to identify and support drought-prone areas.

Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea

Drought is one of the most destructive natural hazards and results in negative effects on the environment, agriculture, economics, and society. A meteorological drought originates from atmospheric components, while a hydrological drought is influenced by properties of the hydrological cycle and generally induced by a continuous meteorological drought. Several studies have attempted to explain the cross dependencies between meteorological and hydrological droughts. However, these previous studies did not consider the propagation of drought classes. Therefore, in this study, to consider the drought propagation concept and to probabilistically assess the meteorological and hydrological drought classes, characterized by the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI), respectively, we employed the Markov Bayesian Classifier (MBC) model that combines the procedure of iteration of feature extraction, classification, and application for assessment of drought classes for both SPI and SRI. The classification results were compared using the observed SPI and SRI, as well as with previous findings, which demonstrated that the MBC was able to reasonably determine drought classes. The accuracy of the MBC model in predicting all the classes of meteorological drought varies from 36 to 76% and in predicting all the classes of hydrological drought varies from 33 to 70%. The advantage of the MBC-based classification is that it considers drought propagation, which is very useful for planning, monitoring, and mitigation of hydrological drought in areas having problems related to hydrological data availability.

일반인 인식조사를 통한 도시가뭄 정책의 개선방향: 한국과 캘리포니아를 대상으로

일반인 인식조사를 통한 도시가뭄 정책의 개선방향: 한국과 캘리포니아를 대상으로

Long-Lead Predictions of Warm Season Droughts in South Korea Using North Atlantic SST

AbstractUnderstanding and predicting warm season (May–October) droughts is critically important in South Korea for agricultural productivity and water resource management. Using a 6-month standardized precipitation index ending in October (SPI6_Oct), we investigate the interannual variability of warm season droughts and the related large-scale atmospheric circulations for the most recent 20-yr period (1995–2014). Cyclonic (anticyclonic) circulations to the east of Japan (in the North Pacific) tend to induce warm season droughts (wetness) by suppressing (enhancing) moist water transport from the south of the Korean Peninsula. These circulation patterns to the east of Japan are linked to a barotropic Rossby wave–like teleconnection pattern from the North Atlantic to East Asia, which is found to be responsible for the interannual variability of SPI6_Oct. This teleconnection pattern is highly correlated with the difference in sea surface temperature (SST) between the Norwegian Sea and the Barents Sea (referred to as NA_dipole) in January–March (r = 0.68), which modulates the snow depth over the Ural Mountains in spring and the sea ice concentration over the Barents Sea during the entire warm season. Two drought prediction models, an empirical model and a hybrid machine learning model, are developed and tested for their predictive skills for South Korea. An empirical prediction model using NA_dipole as one of the predictors is found to accurately capture the interannual variability of SPI6_Oct (r2 = 53%). NA_dipole is found to improve the predictive skills of the hybrid machine learning drought prediction model, especially for longer lead times. Our results emphasize the significant role of North Atlantic SST anomalies in warm season medium-range droughts in South Korea.

An Integrated Framework for Extreme Drought Assessments Using the Natural Drought Index, Copula and Gi* Statistic

This study proposes a framework to evaluate extreme drought using the natural drought index, copula and hot spot analysis. The study area was South Korea. Data were used from 59 automatic synoptic observing system stations; the variable infiltration capacity model was used for the period from 1981 to 2016. The natural drought index was constructed from precipitation, runoff and soil moisture data, which reflect the water cycle. The average interval, duration and severity of extreme drought events were determined following Runs theory. The most extreme drought period occurred in 2014–2016, with 46 of the 59 weather stations exhibiting drought conditions and 78% exhibiting extreme drought conditions. The Inje and Seosan stations exhibited the longest drought duration of 6 months, the most severe drought was 5 times higher than the extreme drought severity threshold. The periods and locations of extreme drought were identified using the natural drought index corresponded to the historical droughts in South Korea. Furthermore, the joint return period of 50 years indicated the northeastern area of South Korea suffered from long-lasting and severe drought. Meanwhile, hot spot analysis was used to explore the extreme drought conditions and showed an increasing trend in the middle and northeastern parts of South Korea. Overall, this framework provides water resource managers with essential information about extreme events, extreme joint return periods, locations and significant trends of natural drought that have not been considered before.

Investigating the influence of natural events and anthropogenic activities on hydrological drought in South Korea

This study is supported by Korea Ministry of Environment (MOE) as “Water Management Research Program (18AWMP-B079625-05)”. The first author would be highly thankful to Higher Education Commission (HEC) and Government of Pakistan for the scholarship under the project “HRD Initiative-MS leading to Ph.D. program of faculty development for UESTPS, Phase-1, and Batch-V for Hanyang University, South Korea”.

Hydrologic Risk Assessment of Future Extreme Drought in South Korea Using Bivariate Frequency Analysis

Recently, climate change has increased the frequency of extreme weather events. In South Korea, extreme droughts are frequent and cause serious damage. To identify the risk of extreme drought, we need to calculate the hydrologic risk using probabilistic analysis methods. In particular, future hydrologic risk of extreme drought should be compared to that of the control period. Therefore, this study quantitatively assessed the future hydrologic risk of extreme drought in South Korea according to climate change scenarios based on the representative concentration pathway (RCP) 8.5. A threshold level method was applied to observation-based rainfall data and climate change scenario-based future rainfall data to identify drought events and extract drought characteristics. A bivariate frequency analysis was then performed to estimate the return period considering both duration and severity. The estimated return periods were used to calculate and compare hydrologic risks between the control period and the future. Results indicate that the average duration of drought events for the future was similar with that for the control period, however, the average severity increased in most future scenarios. In addition, there was decreased risk of maximum drought events in the Yeongsan River basin in the future, while there was increased risk in the Nakdong River basin. The median of risk of extreme drought in the future was calculated to be larger than that of the maximum drought in the control period.

Probabilistic Characteristics of Drought Propagation from Meteorological to Hydrological Drought in South Korea

A reliable understanding of propagation from meteorological to hydrological drought is necessary for accurate forecasting of hydrological droughts. Our current understanding of drought propagation is limited because the characteristics of each drought and the lag time between droughts are uneven due to spatial variability in underlying conditions and climatic variables. The objective of this study is to identify the probabilistic relationship between lag time and the occurrence of different classes of hydrological drought in South Korea while considering propagation factors and using a Bayesian network model. The results show that the propagation rate varied from 27% to 60% and the maximum value of the lag time was projected to be 4 to 10 weeks. For moderate intensity of meteorological drought, the occurrence probability of lag time was high and decreased when the intensity changed to severe and extreme. In addition, the probability for each class of hydrological drought intensity varied with space and increased as the intensity of propagated meteorological drought class changed from moderate to extreme. The observed probabilistic characteristics of hydrological drought may be useful in decision-making strategies for mitigating water shortage.

Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea

The Korea Meteorological Administration uses soil moisture (SM) observed by the Advanced Microwave Scanning Radiometer-2 (AMSR2) to monitor drought. However, it may not be appropriate for monitoring drought in South Korea due to significant underestimation of SM. In this study, we used a deep learning method that performs better than traditional statistical and physical models for reliable estimation of SM based on remotely sensed satellite data. For estimating SM, we carefully selected input variables that exhibit a feedback loop with SM. To build an effective deep learning model, we examined the influences of sampling criteria and input parameters as well as the accuracy of several deep neural networks. The selected model was cross-validated to determine its stability. The estimated SM using deep learning had a high correlation coefficient (R) of 0.89 and a low root mean square error (RMSE; 3.825%) and bias (−0.039%) compared to in-situ measurements. A time series analysis using dynamic time warping was conducted which showed that the estimated SM was almost similar to the in-situ SM. In order to investigate the improvement in SM estimation using our method, it was compared with the Global Land Data Assimilation System and AMSR2. Significant improvements in R and a reduction in error values by more than half were achieved using our method. The estimated SM has finer spatial resolution at 4 km, and it can be rapidly produced, which will be useful for drought monitoring over the Korean Peninsula in near-real-time.

Spatial and Temporal Trend Analysis of Precipitation and Drought in South Korea

High spatial and temporal variation in precipitation in South Korea leads to an increase in the frequency and duration of drought. In this study, the spatial characteristics of temporal trends for precipitation and drought severity time series were analyzed at 55 stations across South Korea for the period 1980–2015. This study also reviewed the usefulness of different trend tests while addressing the issue of serial correlation, which has often received less attention in previous studies. Results showed that most significant trends in precipitation were detected along the south coast of South Korea, especially during winter, late spring and summer, whereas no significant trend was detected in annual precipitation. The Sen’s slope of the trends increased from January to August and decreased from August onward. Principal component analysis applied on Standardized Precipitation Index (SPI) at a 12-month time scale divides the whole of South Korea into four subregions with different temporal behaviors of drought severity. Moreover, drought severity showed a significant increasing trend, mainly on the northeast coast. Drought frequency analysis showed more frequent droughts in late winter, early spring and early autumn, with less frequent droughts in summer.

Probabilistic assessment of meteorological drought over South Korea under RCP scenarios using a hidden Markov model

Most drought indices are evaluated based on pre-defined thresholds, which are inadequate for demonstrating the inherent uncertainty of drought. This study employed a hidden Markov model-based drought index (HMM-DI) for probabilistic assessment of meteorological drought in South Korea. The HMM-DI was developed to take into account the inherent uncertainty embedded in daily precipitation and to assess drought severity without using pre-defined thresholds. Daily rainfall data recorded during 1973–2015 at 56 stations over South Korea were aggregated with 6- and 12-month windows to develop HMM-DIs for various time scales. The HMM-DIs were extended to assess future droughts in South Korea using synthesized monthly rainfall data (2016–2100) under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The overall results indicated that the HMM-DI can classify drought conditions considering inherent uncertainty embedded in observations and can also demonstrate the probabilistic drought occurrence in the future.

Spatial and Temporal Aspects of Drought in South Korea based on Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI)

This study investigated the occurrence and intensity of drought in South Korea between 1951 and 2012, using two drought indices; Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Spatial distributions of both drought indices over East Asia was examined to determine the possible association of drought occurrence in South Korea with its neighboring regions. Based on 3-month and 12-month SPI measured throughout the analysis period, drought occurs 34 and 14 times in South Korea. Drought frequency was significant at 8－10 months, and 2－3, 6－7, and 15 years for the 3-month and 12-month SPI, respectively, based on wavelet analysis. Drought events were also determined by PDSI and were similar to those evaluated by SPI before 2000; however, drought occurred more frequently after 2000. Across East Asia, 12-month SPI showed that the drought occurrence in South Korea was associated with the wet weather in the southern China, and based on PDSI, South Korea drought events were also associated with drought in central China. The difference between SPI and PDSI results indicates that multiple drought indices are necessary for better analysis and prediction of drought in South Korea.