Moon Seong Kang

Bias Correction of RCP-based Future Extreme Precipitation using a Quantile Mapping Method ; for 20-Weather Stations of South Korea

The objective of this study was to correct the bias of the Representative Concentration Pathways (RCP)-based future precipitation data using a quantile mapping method. This method was adopted to correct extreme values because it was designed to adjust simulated data using probability distribution function. The Generalized Extreme Value (GEV) distribution was used to fit distribution for precipitation data obtained from the Korea Meteorological Administration (KMA). The resolutions of precipitation data was 12.5 km in space and 3-hour in time. As the results of bias correction over the past 30 years (1976~2005), the annual precipitation was increased 16.3 % overall. And the results for 90 years (divided into 2011~2040, 2041~2070, 2071~2100) were that the future annual precipitation were increased 8.8 %, 9.6 %, 11.3 % respectively. It also had stronger correction effects on high value than low value. It was concluded that a quantile mapping appeared a good method of correcting extreme value.

Water Balance in Irrigation Reservoirs Considering Flood Control and Irrigation Efficiency Variation

AbstractThe combined effect of limited water resources and ever-growing water demand necessitates the use of more-accurate approaches for reservoir water-balance analyses. Therefore, this study aims to develop a new operational scheme by considering flood control and variations in irrigation efficiency for simulating reservoir water-balance components. This study is conducted on three reservoirs in Korea, which were monitored for 4xa0years. Based on the findings of the sensitivity analyses, irrigation efficiency variation appears to be most sensitive to the outflow variables and was thus calibrated on a yearly basis. Compared with the two other schemes, i.e.,xa0with respective exclusions of flood control and irrigation efficiency variations, the new scheme performs better in simulating reservoir water levels. Additionally, it is found that without the consideration of the newly developed scheme, significant errors could occur in the simulation of water-balance components, which could lead to incorrect decisio...

Simulation of Agricultural Water Supply Considering Yearly Variation of Irrigation Efficiency

The objective of this study was to evaluate simulation of agricultural water supply considering yearly variation of irrigation efficiency. The water supply data of the Idong reservoir from 2001 through 2009 was collected and used for this study. Total 6 parameters including irrigation efficiency (Es), drainage outlet height, and infiltration, were used for sensitivity analysis, calibration, and validation. Among the parameters, the Es appeared to be the most sensitivity parameter. The Es was calibrated on a yearly basis considering sensitivity and time-varying characteristic, while other parameters were set to fixed values. The statistics of percent bias (PBLAS), Nash-Sutcliffe efficiency (NSE), and root means square error to the standard deviation of measured data (RSR) for a monthly step were 2.7%, 0.93, and 0.26 for the calibration, and 3.9%, 0.89, and 0.32 for the validation, correspondently. The results showed a good agreement with the observations. This implies that the modeling only with appropriate parameter values, apart from modeling approaches, can simulate the real supply operation reasonably well. However, the simulations with uncalibrated parameters from previous studies produced poor results. Thus, it is important to use calibrated values, and especially, we suggest the Es`s yearly calibration for simulating agricultural water supply.

Long-term evaluation of the BMPs scenarios in reducing nutrient surface loads from paddy rice cultivation in Korea using the CREAMS-PADDY model

Curbing nutrient loads from rice cultivation has been an issue for the water quality management of surface water bodies in the Asian monsoon region. The objectives of this study were to develop paddy BMP scenarios and to evaluate their effectiveness on nutrient loads reduction using long-term model simulation. Totally five BMP scenarios were developed based on the three paddy farming factors of drainage outlet height, fertilizer type, and application amount and were compared with conventional practices. CREAMS-PADDY model was chosen for the paddy nutrient simulation, and two-year field experimental data were used for the model calibration and validation. The validated model was used to evaluate the developed BMP scenarios for the 46xa0years of simulation period. The observed nutrient loads were 15.2 and 1.45xa0kg/ha for nitrogen and phosphorus, respectively, and mainly occurred by early season drainage and rainfall runoff in summer. The long-term simulation showed that the soil test-based fertilization and drainage outlet raising practice were the two most effective methods in nutrient loads reduction. The combination of these two resulted in the greatest loads reduction by 29 and 37xa0% for T-N and T-P, respectively (p valuexa0<xa00.001). Overall the effectiveness of the BMP scenarios was decreased in the wet season. As the conclusion, outlet height control and soil nutrient-based fertilization were suggested as the effective practices in paddy loads reduction and their combination can be a practicable BMP scenario for the paddy nutrient management.

Impact of tilling on biosolids drying and indicator microorganisms survival during solar drying process

As biosolids application to croplands becomes a common practice, potential harm from pathogenic microbes needs to be mitigated for its safe reuse. The objective of this study was to investigate the impacts of tilling treatment on biosolids drying and microbial inactivation during the solar drying process in a semi-arid and temperate region. Solar drying experiments were conducted in sand and gravel dying beds open-to-the-air and under covering structures with biosolids to 20 cm depth from 2004 to 2006. Anaerobically- and Aerobically-digested biosolids received different tilling treatments throughout the drying process, while a series of biosolids samples were collected to determine the impact on total solids and microbial concentrations (Salmonella spp and heminth ova). Tilling treatments appeared to enhance the biosolids drying and microbial inactivation. Tilling was more effective during the cold season compared with the summer season and tilling treatments were also helpful in elevating biosolids temperature by expediting biosolids drying. The combined effect of temperature increase and moisture decrease by tilling may have resulted in faster microbial inactivation, particularly for persistent helminth ova. It was concluded that incorporation of tilling into biosolids solar drying can expedite biosolids drying as well as microbial inactivation, and thus can be an effective measure for shortening the biosolids conversion to Class A biosolids in which pathogens are reduced to below detectable levels.

Flood Risk Assessment Based on Bias-Corrected RCP Scenarios with Quantile Mapping at a Si-Gun Level

The main objective of this study was to evaluate Representative Concentration Pathways (RCP) scenarios-based flood risk at a Si-Gun level. A bias correction using a quantile mapping method with the Generalized Extreme Value (GEV) distribution was performed to correct future precipitation data provided by the Korea Meteorological Administration (KMA). A series of proxy variables including CN80 (Number of days over 80 mm) and CX3h (Maximum precipitation during 3-hr) etc. were used to carry out flood risk assessment. Indicators were normalized by a Z-score method and weighted by factors estimated by principal component analysis (PCA). Flood risk evaluation was conducted for the four different time periods, i.e. 1990s, 2025s, 2055s, and 2085s, which correspond to 1976~2005, 2011~2040, 2041~2070, and 2071~2100. The average flood risk indices based on RCP4.5 scenario were 0.08, 0.16, 0.22, and 0.13 for the corresponding periods in the order of time, which increased steadily up to 2055s period and decreased. The average indices based on RCP8.5 scenario were 0.08, 0.23, 0.11, and 0.21, which decreased in the 2055s period and then increased again. Considering the average index during entire period of the future, RCP8.5 scenario resulted in greater risk than RCP4.5 scenario.

Estimating regional productivity based on demographic structure with artificial neural network

This paper explores the impact of the age structure on regional productivity. An estimation model based on artificial neural network (ANN) was developed on the assumption that demographic change, due to aging and migration has a significant effect on the regional productivity, especially in rural regions. A multilayer perceptron ANN model was applied to consider the composition of demographic structure rather than ratio between two population groups such as aged-child ratio. Regional productivity was estimated by applying the estimation model developed in this research study to population and aggregate product data of sixteen South Korean cities and counties, from 2000 to 2011. Developed model is trained with data of sixteen cities and counties, from 2000 to 2009, and verified with observation data and estimation results of 2010 and 2011. The results revealed that gross regional domestic product per capita, which represents regional productivity, is significantly related to demographic structure and can be estimated by age structure.