• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.225-236
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• 2024

To prepare for the impending climate crisis, it is necessary to establish policies and strategies based on scientific predictions and analyses of climate change impacts. For this, climate change should be considered, however, in conventional scenario-led approach, researchers select and utilize representative climate change scenarios. Using the representative climate change scenarios makes prediction results high uncertain and low reliable, which leads to have limitations in applying them to relevant policies and design standards. Therefore, it is necessary to utilize scenario-neutral approach considering possible change ranges due to climate change. In this study, hydrologic risk was estimated for Boryeong after generating 343 time series of climate stress and calculating drought return period from bivariate drought frequency analysis. Considering 18 scenarios of SSP1-2.6 and 18 scenarios of SSP5-8.5, the results indicated that the hydrologic risks of drought occurrence with maximum return period ranged 0.15±0.025 within 20 years and 0.3125±0.0625 within 50 years, respectively. Therefore, it is necessary to establish drought policies and countermeasures in consideration of the corresponding hydrologic risks in Boryeong.

• Journal of Korea Water Resources Association
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• v.52 no.8
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• pp.545-554
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• 2019

The most drought assessments are based on a drought index, which depends on univariate variables such as precipitation and soil moisture. However, there is a limitation in representing the drought conditions with single variables due to their complexity. It has been acknowledged that a multivariate drought index can more effectively describe the complex drought state. In this context, this study propose a Copula-based drought index that can jointly consider precipitation and soil moisture. Unlike precipitation data, long-term soil moisture data is not readily available so that this study utilized a Gaussian Mixture Non-Homogeneous Hidden Markov chain Model (GM-NHMM) model to simulate the soil moisture using the observed precipitation and temperature ranging from 1973 to 2014. The GM-NHMM model showed a better performance in terms of reproducing key statistics of soil moisture, compared to a multiple regression model. Finally, a bivariate frequency analysis was performed for the drought duration and severity, and it was confirmed that the recent droughts over Jeollabuk-do in 2015 have a 20-year return period.

• Economic and Environmental Geology
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• v.41 no.4
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• pp.443-451
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• 2008

Memon(1995) pointed out that the groundwater recharge from the precipitation is affected by various factors such as the occurrence, intensity, duration, and seasonal distribution of rainfall; air temperature, humidity, and wind velocity; the character and thickness of the soil layer above the water table; vegetated cover, soil moisture content, depth to the water table, topography; and land use. To reflect above factors, groundwater recharge in Chungju basin is computed by using the SWAT-K which is a longterm continuous watershed hydrologic model. Frequency analysis is adopted to evaluate the existing values of potential amount of groundwater development which is made by the 10 year drought frequency rainfall multiplied by recharge coefficient. In this work, the recharge rates of 10 year drought frequency in subbains were computed and compared with the existing values of potential amount of groundwater development. This process could point out the problems of existing precesses used for computing potential amount of groundwater development.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2004.11a
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• pp.73-73
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• 2004

• Proceedings of the Korea Water Resources Association Conference
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• 1998.05a
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• pp.68-75
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• 1998

• Magazine of the Korean Society of Agricultural Engineers
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• v.21 no.4
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• pp.108-117
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• 1979

In order to obtain the basic data for design of water structures which can be contributed to the planning of water use. Best fitted distribution function and the equations for the probable minimum flow were derived to the annual minimum flow of five subwatersheds along Geum River basin. The result were analyzed and summarized as follows. 1. Type III extremal distribution was considered as a best fit one among some other distributions such as exponential and two parameter lognormal distribution by $x^2$-goodness of fit test. 2. The minimum flow are analyzed by Type III extremal distribution which contains a shape parameter $\lambda$, a location parameter ${\beta}$ and a minimum drought $\gamma$. If a minimum drought $\gamma=0$, equations for the probable minimum flow, $D_T$, were derived as $D_T={\beta}e^{\lambda}1^{y'}$, with two parameters and as $D_T=\gamma+(\^{\beta}-\gamma)e^{{\lambda}y'}$ with three parameters in case of a minimum drought ${\gamma}>0$ respectively. 3. Probable minimum flow following the return periods for each stations were also obtained by above mentioned equations. Frequency curves for each station are drawn in the text. 4. Mathematical equation with three parameters is more suitable one than that of two parameters if much difference exist between the maximum and the minimum value among observed data.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.166-166
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• 2023

Compound hazards (CHs) are two or more extreme climate events combined which occur simultaneously in the same region at the same time. Compared to individual hazards, the combination of hazards that cause CHs can result in greater economic losses and deaths. While several extreme climate events have been recorded across Asia for the past decades, many studies have only focused on a single hazard. In this study, we assess the spatiotemporal pattern of dry compound hazards which includes drought, heatwave, fire and wind across Asia for the last 42 years (1980-2021) using the historical data from ERA5 Reanalysis dataset. We utilize a daily spatial data of each climate event to assess the occurrence of such compound hazards on a daily basis. Heatwave, fire and wind hazard occurrences are analyzed using daily percentile-based thresholds while a pre-defined threshold for SPI is applied for drought occurrence. Then, the occurrence of each type of compound hazard is taken from overlapping the map of daily occurrences of a single hazard. Lastly, a multivariate assessment are conducted to quantify the occurrence frequency, hotspots and trends of each type of compound hazard across Asia. By conducting a multivariate analysis of the occurrence of these compound hazards, we identify the relationships and interactions in dry compound hazards including droughts, heatwaves, fires, and winds, ultimately leading to better-informed decisions and strategies in the natural risk management.

• Atmosphere
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• v.31 no.5
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.577-587
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• 2021

Drought is generally considered to be a natural disaster caused by accumulated water shortages over a long period of time, taking months or years and slowly occurring. However, climate change has led to rapid changes in weather and environmental factors that directly affect agriculture, and extreme weather conditions have led to an increase in the frequency of rapidly developing droughts within weeks to months. This phenomenon is defined as 'Flash Drought', which is caused by an increase in surface temperature over a relatively short period of time and abnormally low and rapidly decreasing soil moisture. The detection and analysis of flash drought is essential because it has a significant impact on agriculture and natural ecosystems, and its impacts are associated with agricultural drought impacts. In South Korea, there is no clear definition of flash drought, so the purpose of this study is to identify and analyze its characteristics. In this study, flash drought detection condition was presented based on the satellite-derived drought index Evaporative Stress Index (ESI) from 2014 to 2018. ESI is used as an early warning indicator for rapidly-occurring flash drought a short period of time due to its similar relationship with reduced soil moisture content, lack of precipitation, increased evaporative demand due to low humidity, high temperature, and strong winds. The flash droughts were analyzed using hydrometeorological characteristics by comparing Standardized Precipitation Index (SPI), soil moisture, maximum temperature, relative humidity, wind speed, and precipitation. The correlation was analyzed based on the 8 weeks prior to the occurrence of the flash drought, and in most cases, a high correlation of 0.8(-0.8) or higher(lower) was expressed for ESI and SPI, soil moisture, and maximum temperature.

• 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.