• Journal of Soil and Groundwater Environment
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• v.21 no.6
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• pp.22-35
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• 2016

Global climate change could have an impact on hydrological process of a watershed and result in problems with future water supply by influencing the recharge process into the aquifer. This study aims to assess the change of groundwater recharge rate by climate change and to predict the sustainability of groundwater resource in Pyoseon watershed, Jeju Island. For the prediction, the groundwater recharge rate of the study area was estimated based on two future climate scenarios (RCP 4.5, RCP 8.5) by using the Soil Water Balance (SWB) computer code. The calculated groundwater recharge rate was used for groundwater flow simulation and the change of groundwater level according to the climate change was predicted using a numerical simulation program (FEFLOW 6.1). The average recharge rate from 2020 to 2100 was predicted to decrease by 10~12% compared to the current situation (1990~2015) while the evapotranspiration and the direct runoff rate would increase at both climate scenarios. The decrease in groundwater recharge rate due to the climate change results in the decline of groundwater level. In some monitoring wells, the predicted mean groundwater level at the year of the lowest water level was estimated to be lower by 60~70 m than the current situation. The model also predicted that temporal fluctuation of groundwater recharge, runoff and evapotranspiration would become more severe as a result of climate change, making the sustainable management of water resource more challenging in the future. Our study results demonstrate that the future availability of water resources highly depends on climate change. Thus, intensive studies on climate changes and water resources should be performed based on the sufficient data, advanced climate change scenarios, and improved modeling methodology.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.1035-1044
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• 2008

It is critical to study on data charateristics analysis and prediction for the flood disaster prevention and water quality monitoring because discharge and TOC data in a river channel are strongly nonlinear. Therefore, in the present study, prediction models for discharge, TOC, and TOC load data were developed using approximation component in the last level and detail components segregated by wavelet transform. The results show that the developed model overcame the persistence phenomenon which could be seen from previous models and improved the prediciton accuracy comparing with the previous models. It might be expected that the results from the present study can mitigate flood disaster damage and construct active alternatives to various water quality problems in the future.

• Korean Journal of Soil Science and Fertilizer
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• v.41 no.3
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• pp.228-234
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• 2008

The purpose of this study was to predict environmental distribution of anthracene, benzene, benzo[a]pyrene, 1-methylphenanthrene and phenanthrene in a four phase biopile system - air, water, soil and non aqueous phase liquid (NAPL) phase using level I fugacity model. Soil samples used for this study were collected from three sites in the United Kingdom which were historically contaminated with petroleum hydrocarbons. The level I fugacities (f) for the five contaminants were markedly different, however, the fugacities of each contaminant in three soil samples did not show significant difference. NAPL and soil were the dominant phases for all five contaminants. Results of this study indicated that difference in percentage of organic carbon strongly influenced the partitioning behavior of the cntaminants. The presence of benzene calls for an urgent need for risk-based management of air and water phase. Whereas insignificant amount of chemicals leached in the water phase for other organic contaminants showing greatly reduced potential of groundwater contamination. Furthermore, this study helped us to confirm the association of risk critical contaminants with the residual saturation in treated soils. They also can be used to emphasize the importance of accounting for the partitioning behavior of both NAPL and soil phases in the process of the risk assessment of the sites contaminated with petroleum hydrocarbons.

• Korean Journal of Agricultural Science
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• v.41 no.4
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• pp.391-398
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• 2014

Identifying soil water content as a major factor for evaluating irrigation and water resource is a primary module to develop a prediction model. A variety of PTFs (Pedo-Transfer Functions) are applied in the models to estimate soil water content, the analysis techniques, however, which compare the estimated from models and the measured by instruments, are not reached at the level to demonstrate the effectiveness of the PTFs in Korea. Many soil physicians such as Eom, Peterson, Rawls, Saxton, Bruand, Baties, Tomasella & Hodnett (T&H), and Minasny, have developed analytic models using PTFs. Soil data for the analysis used soil water contents on 347 soil series (10 kPa), 358 soil series (33 kPa), 356 soil series (1,500 kPa) established by NAAS (National Academy of Agricultural Science). A coefficient of determination on soil water content at 10, 33 and 1,500 kPa was the highest as 0.5932 in EM (Eom model), 0.6744 in REM (Rawls model) and 0.6108 in REM, respectively. In conclusion, it is strongly suggested that the use of EM or REM is suitable for estimating soil water content in Korea although SM (Saxton model) has been widely used.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.107-113
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• 2019

The flood forecasting model currently used in Korea calculates the runoff of basin using the lumped rainfall-runoff model and estimates the river level using the river and reservoir routing models. The lumped model assumes homogeneous drainage zones in the basin. Therefore, it can not consider various spatial characteristics in the basin. In addition, the rainfall data used in lumped model also has the same limitation because of using the point scale rainfall data. To overcome the limitations as mentioned above, many researchers have studied to apply the distributed rainfall-runoff model to flood forecasting system. In this study, to apply the Grid-based Rainfall-Runoff Model (GRM) to the Korean flood forecasting system, the optimal resolution is determined by analyzing the difference of the results of the runoff according to the various resolutions. If the grid size is to small, the computation time becomes excessive and it is not suitable for applying to the flood forecasting model. Even if the grid size is too large, it does not fit the purpose of analyzing the spatial distribution by applying the distributed model. As a result of this study, the optimal resolution which satisfies the accuracy of the bsin runoff prediction and the calculation speed suitable for the flood forecasting was proposed. The accuracy of the runoff prediction was analyzed by comparing the Nash-Sutcliffe model efficiency coefficient (NSE). The optimal resolution estimated from this study will be used as basic data for applying the distributed rainfall-runoff model to the flood forecasting system.

• Proceedings of the Korean Society of Crop Science Conference
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• 2019.09a
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• pp.19-19
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• 2019

It is necessary to maintain stable crop productions under the unsuitable environments, because the drought and flood may be frequently caused by the global warming. Therefore, it is agent to improve the crop growth model corresponded to soil moisture status. Chili pepper (Capsicum annuum) is one of the useful crop in Asia, and then it is affected by change of precipitation in consequence drought and flood occur however crop model to evaluate water stresses on chili pepper is not enough yet. In this study, development of crop model under different soil moisture status was attempted. The experiment was conducted on the slope fields in the greenhouse. The water level was kept at 20cm above the bottom of the container. Habanero (C. chinense) was used as material for crop model. Sap bleeding rate, SPAD value, chlorophyll content, stomatal conductance, leaf water potential, plant height, leaf area and shoot dry weight were measured at 10 days after treatment (DAT) and 13 DAT. Moreover, temperature and RH in the greenhouse, soil volume water contents (VWC) and soil water potential were measured. As a result, VWC showed 4.0% at the driest plot and 31.4% at the wettest plot at 13 DAT. The growth model was calculated using WVC and the growth analysis parameters. It was considered available, because its coefficient of determination showed 0.84 and there are significant relationship based on plants physiology among the parameters and the changes over time. Furthermore, we analyzed the important factors for higher accuracy prediction using multiple regression analysis.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.488-494
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• 2023

In this paper, scenario-based urban flood prediction for the entire Jinju city was performed, and a simulation domain was constructed using G2D as a 2-dimensional urban flood analysis model. The domain configuration is DEM, and the land cover map is used to set the roughness coefficient for each grid. The input data of the model are water level, water depth and flow rate. In the simulation of the built G2D model, virtual rainfall (3 mm/10 min rainfall given to all grids for 5 hours) and virtual flow were applied. And, a GPU acceleration technique was applied to determine whether to run the flood analysis model in the target area. As a result of the simulation, it was confirmed that the high-resolution flood analysis time was significantly shortened and the flood depth for visual flood judgment could be created for each simulation time.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.799-811
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• 2006

As an alternative measure to replace reactive stance with proactive one, a risk based management scheme has been commonly applied to enhance public satisfaction on water service by providing a higher creditable solution to handle a rehabilitation problem of pipe having high potential risk of leaks. This study intended to examine the feasibility of a simulation model to predict a recurrence probability of pipe leaks. As a branch of the data mining technique, probabilistic neural network (PNN) algorithm was applied to infer the extent of leaking recurrence probability of water network. PNN model could classify the leaking level of each unit segment of the pipe network. Pipe material, diameter, C value, road width, pressure, installation age as input variable and 5 classes by pipe leaking probability as output variable were built in PNN model. The study results indicated that it is important to pay higher attention to the pipe segment with the leak record. By increase the hydraulic pipe pressure to meet the required water demand from each node, simulation results indicated that about 6.9% of total number of pipe would additionally be classified into higher class of recurrence risk than present as the reference year. Consequently, it was convinced that the application of PNN model incorporated with a data base management system of pipe network to manage municipal water distribution network could make a promise to enhance the management efficiency by providing the essential knowledge for decision making rehabilitation of network.

• Journal of Environmental Science International
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• v.26 no.9
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• pp.1031-1043
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• 2017

Recently severe drought caused the water shortage around the western parts of Chungcheongnamdo province, South Korea. A Diversion tunnel from the Geum river to the Boryong dam, which is the water supply dam for these areas has been proposed to solve this problem. This study examined hydraulic impacts on the Geum river associated with the diversion plan assuming the severe drought condition of 2015 would persist for the simulation period of 2016. The hydraulic simulation model was verified using hydrologic and hydraulic data including hourly discharges of the Geum river and its 8 tributaries, fluctuation of tidal level at the mouth of the river, withdrawals and return flows and operation records of the Geum river barrage since Feb. 1, 2015 through May 31, 2015. For the upstream boundary condition of the Geum river predicted inflow series using the nonlinear regression equation for 2015 discharge data was used. In order to estimate the effects of uncertainty in inflow prediction to the results total four inflow series consisting of upper limit flow, expected flow, lower limit flow and instream flow were used to examine hydraulic impacts of the diversion plan. The simulation showed that in cases of upper limit and expected flows there would be no problem in taking water from the Geum river mouth with a minimum water surface level of EL(+) 1.44 m. Meanwhile, the simulation also showed that in cases of lower limit flow and instream flow there would be some problems not only in taking water for water supply from the mouth of the Geum river but also operating the diversion facility itself with minimum water surface levels of EL(+) 0.94, 0.72, 0.43, and 0.14 m for the lower limit flow without/with diversion and the instream flow without/with diversion, respectively.

• Journal of KIISE
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• v.44 no.6
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• pp.607-612
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• 2017

This paper presents an attempt to apply Deep Learning technology to solve the problem of forecasting floods in urban areas. We employ Recurrent Neural Networks (RNNs), which are suitable for analyzing time series data, to learn observed data of river water and to predict the water level. To test the model, we use water observation data of a station in the Trinity river, Texas, the U.S., with data from 2013 to 2015 for training and data in 2016 for testing. Input of the neural networks is a 16-record-length sequence of 15-minute-interval time-series data, and output is the predicted value of the water level at the next 30 minutes and 60 minutes. In the experiment, we compare three Deep Learning models including standard RNN, RNN trained with Back Propagation Through Time (RNN-BPTT), and Long Short-Term Memory (LSTM). The prediction quality of LSTM can obtain Nash Efficiency exceeding 0.98, while the standard RNN and RNN-BPTT also provide very high accuracy.