• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.1011-1021
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• 2015

This study aims to investigate droughts from the magnitude perspective based on the SPI (Standardized Precipitation Index) and the theory of runs applicable to quantitative analysis of drought in South Korea. In addition, the dry spell analysis was conducted on the drought history in the five major river basins of South Korea to obtain the magnitude, duration and severity of drought, and the quantitative evaluation has been made on historical droughts by estimating the return period using the SDF (Severity-Duration-Frequency) curve gained through drought frequency analysis. The analysis results showed that the return periods for droughts at the regional and major river basin scales were clearly identified. The return periods of severe drought that occurred around the major river basins in South Korea turn out to be mostly 30 to 50 years with the years of the worst drought in terms of severity being 1988 and 1994. In particular, South Korea experienced extremely severe droughts for two consecutive years during the period between 1994 and 1995. Drought in 2014 occurred in the Han River basin and was evaluated as the worst one in terms of severity and magnitude.

• Journal of Korea Water Resources Association
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• v.48 no.7
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• pp.581-593
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• 2015

This study constructs the drought outlook system using ESP(Ensemble Streamflow Prediction) method and evaluates its utilization for drought prediction. Historical Runoff(HR) was estimated by employing LSM(Land Surface Model) and the observed meteorological, hydrological and topographical data in South Korea. Also Predicted Runoff(PR) was produced for different lead times(i.e. 1-, 2-, 3-month) using 30-year past meteorological data and the initial soil moisture condition. The HR accuracy was higher during MAM, DJF than JJA, SON, and the prediction accuracy was highly decreased after 1 month outlook. SRI(Standardized Runoff Index) verified for the feasibility of domestic drought analysis was used for drought outlook, and PR_SRI was evaluated. The accuracy of PR_SRI with lead times of 1- and 2-month was highly increased as it considered the accumulated 1- and 2-month HR, respectively. The Correlation Coefficient(CC) was 0.71, 0.48, 0.00, and Root Mean Square Error(RMSE) was 0.46, 0.76, 1.01 for 1-, 2- and 3-month lead times, respectively, and the accuracy was higher in arid season. It is concluded that ESP method is applicable to domestic drought prediction up to 1- and 2-month lead times.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.691-701
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• 2017

The objective of this study is to evaluate the long-term drought outlook information based on long-term forecast data for the 2015 drought event. In order to estimate the Standardized Precipitation Index (SPI) for different durations (3-, 6-, 9-, 12-months), we used the observation precipitation of 59 Automated Synoptic Observing System (ASOS) sites, forecast and hindcast data of GloSea5. The Receiver Operating Characteristic (ROC) analysis and statistical analysis (Correlation Coefficient, CC; Root Mean Square Error, RMSE) were used to evaluate the utilization of drought outlook information for the forecast lead-times (1~6months). As a result of ROC analysis, ROC scores of SPI(3), SPI(6), SPI(9) and SPI(12) were estimated to be over 0.70 until the 2-, 3-, 4- and 5-months. The CC and RMSE values of SPI(3), SPI(6), SPI(9) and SPI(12) for forecast lead-time were estimated as (0.60, 0.87), (0.72, 0.95), (0.75, 0.95) and (0.77, 0.89) until the 2-, 4-, 5- and 6-months respectively.

• Asian Journal of Turfgrass Science
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• v.22 no.1
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• pp.1-12
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• 2008

Remote sensing using multispectral radiometry may be a useful tool to detect drought stress in turf. The objective of this research was to investigate the correlation between drought stress and multispectral reflectance (MSR) from the turf canopy. St. Augustinegrass (Stenotaphrum secundatum[Walt.] Kuntze.) cultivars 'Floratam' and 'Palmetto', 'SeaIsle 1' seashore paspalum Paspalum vaginatum Swartz.), 'Empire' zoysiagrass (Zoysia japonica Steud.), and 'Pensacola' bahiagrass (Paspalum notatumFlugge) were established in lysimeters in the University of Florida Envirotron greenhouse facility in Gainesville. Irrigation was applied at 100%, 80%, 60%, or 40% of evapotranspiration (ET). Weekly evaluations included: a) shoot quality, leaf rolling, leaf firing b) soil moisture, chlorophyll content index; c) photosynthesis and d) multispectral reflectance. All the measurements were correlated with MSR data. Drought stress affected the infrared spectral region more than the visible spectral region. Reflectance sensitivity to water content of leaves was higher in the infrared spectral region than in the visible spectral region. Grasses irrigated at 100% and 80% of ET had no differences in normalized difference vegetation indices (NDVI), leaf area index (LAI), and stress indices. Grasses irrigated at 60% and 40% of ET had differences in NDVI, LAI, and stress indices. All measured wavelengths except 710nm were highly correlated (P < 0.0001) with turf visual quality, leaf firing, leaf rolling, soil moisture, chlorophyll content index, and photosynthesis. MSR could detect drought stress from the turf canopy.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.83-93
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• 2020

East Asia, which includes China, Japan, Korea, and Mongolia, is highly impacted by hydroclimate extremes such drought, flood, and typhoon recent year. In 2017, more than 18.5 million hectares of crops have been damaged in China, and Korea has suffered economic losses as a result of severe drought. Satellite-derived rainfall products are becoming more accurate as space and time resolution become increasingly higher, and provide an alternative means of estimating ground-based rainfall. In this study, we verified the availability of rainfall products by comparing widely used satellite images such as Climate Hazards Groups InfraRed Precipitation with Station (CHIRPS), Global Precipitation Climatology Centre (GPCC), and Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) with ground stations in East Asia. Also, the satellite-based rainfall products were used to calculate the Standardized Precipitation Index (SPI). The temporal resolution is based on monthly images and compared with the past 30 years data from 1989 to 2018. The comparison between rainfall data based on each satellite image products and the data from weather station-based weather data was shown by the coefficient of determination and showed more than 0.9. Each satellite-based rainfall data was used for each grid and applied to East Asia and South Korea. As a result of SPI analysis, the RMSE values of CHIRPS were 0.57, 0.53 and 0.47, and the MAE values of 0.46, 0.43 and 0.37 were better than other satellite products. This satellite-derived rainfall estimates offers important advantages in terms of spatial coverage, timeliness and cost efficiency compared to analysis for drought assessment with ground stations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.743-750
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• 2019

Most of the water supply facilities that use rivers as sources do not have monitoring facilities such as precipitation and stream flow measurement, and there is no judgment standard for drought response such as water intake control in river flow during dry season. In addition, it was confirmed that local government officials, who deal with actual drought work, have limitations in applying the drought index (SPI, PDSI, etc.) and diffusion models that have been proposed so far in advance. Therefore, in this study, the drought prediction system was constructed to determine the number of water-intake available days using SWAT (Soil and Water Assessment Tool) and the water supply network from the intake source to the beneficiary area, suggesting the drought spreading time and space.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.293-303
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• 2016

This study implemented a comparison of SPI characteristics in terms of quantitative and spatial analysis depending on four RCP scenarios. For this purpose, we compared quantitative characteristics of drought using standard precipitation index resulted from daily precipitation data reflecting future green gas concentration scenarios, and spatial distribution field of seasonal drought occurrence frequency and its duration, was analyzed to compare drought trends depending on the RCP scenarios. As a result, we found that SPI time series was quite different from each other and correlation coefficients were lower than 0.08. Depending on the RCP scenarios, spatial distribution results showed different trends in drought severity, frequency, and duration. The biggest reason of the difference is daily precipitation data based on the different greenhouse gas concentrations, but we could not find the effect of the concentration extent on drought occurrence projection. In addition, according to the results from this study, drought analysis results using single RCP scenario may have considerable uncertainty.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.1
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• pp.64-79
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• 2016

The purpose of this study is to find the relationship between precipitation deficit, SPI(standardized precipitation index)-12 month, agricultural reservoir water storage deficit and agricultural drought-related big data, and to evaluate the usefulness of agricultural risk management through big data. For the long term drought (from January 2014 to September 2015), each data was collected and analysed with monthly and Provincial base. The minimum SPI-12 and maximum reservoir water storage deficit compared to normal year were occurred at the same time of July 2014, and August and September 2015. The maximum frequency of big data was occurred at June and July of 2014, and March and June to September of 2015. The maximum big data was occurred 1 month advanced in 2014 and 2 months advanced in 2015 than the maximum reservoir water storage deficit. The occurrence of big data was sensitive to spring drought from March, late Jangma of June, dry Jangma of July and the rainfall deficit of September 2015. The big data was closely related with the meteorological drought and agricultural drought. Because the big data is the in situ feeling drought, it is proved as a useful indicator for agricultural risk management.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.131-140
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• 2020

The purpose of this study is to analyze the possibility of future droughts by calculating the Standardized Groundwater level Index(SGI) after predicting groundwater level using Long Short Term Memory (LSTM) model. The groundwater level of the Kumho River basin was predicted for the next three years by using the LSTM model, and it was validated through RMSE after learning with observation data except the last three years. The temporal SGI was calculated by using the prediction data and the observation data. The calculated SGI was interpolated within the study area, and the spatial SGI was calculated as the average value for each catchment using the interpolated SGI. The possibility of spatio-temporal drought was analyzed using calculated spatio-temporal SGI. It is confirmed that there is a spatio-temporal difference in the possibility of drought. Through the improvement of deep learning model and diversification of validation method, it is expected to obtain more reliable prediction results and the expansion of study area can be used to respond to drought nationwide, and furthermore it can provide important information for future water resource management.

• Journal of agriculture & life science
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• v.50 no.2
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• pp.73-82
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• 2016

Best drought tolerance index was determined through statistics analysis and growth appearance of drought tolerant plants was determined by cultivation in pot and sloping land. For determination of best drought tolerant indicators, RD(Resistant dry days), LD(Leaf area), UTR(Unit transpiration), RWC(Relative water content), RWL(Relative water loss), LA(Leaf area), SN(Stoma unmber) and SA(Stoma area) were carried out by correlation and PCA analysis. RWL and UTR were affected on plant drought tolerance according to comparison among six indices for resistant dry days. The PCs axes separated SA, LA, RD and RWC and SN. UTR was negatively correlated with SA, RWL were also negatively correlated with RWC and SN. RWL and UTR were proved best selection indicator for the selection of drought tolerant species. Ulmus parvifolia, Bidens bipinnata, Patrinia villosa, Kummerowia striata, Arundinella hirta, Artemisia gmelini etc. were selected drought tolerant plants. Shoot growth appearance of drought resistant plants was differed pot and sloping land. Shoot growth and leaf number was no significant differences between the pot and sloping land. However, root growth of drought tolerant plants was all the difference between two cultivation. T/R ratio of drought tolerant plants was also found a big difference. T/R ratio of drought tolerant plants in sloping land was lower than that of pot. These results will be served efficiently plant breeding.