• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.107-118
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• 2014

The objective of this study is to suggest a method for estimating rainfall-runoff relationship using runoff analysis with satellite rainfall and global geographic data for the region due to lack of observed data. This study uses CMORPH and GSMaP_NRT as satellite rainfall data, and GTOPO30 and GLCC as global geographic data. IFAS is used for runoff modeling. In the evaluation of rainfall data, the correlation coefficients of CMORPH and GSMaP_NRT with observed data are 0.37 and 0.30 respectively. Calculated peak runoffs using IFAS show small relative errors with observed data in case of parameters are not calibrated with satellite rainfall data. Therefore, the methods suggested in this study could be applied to ungauged watershed. In the future, this study will analyze runoff for North Korea, a representative inaccessible region, using satellite rainfall and global geographic data.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.315-325
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• 2016

Generally, a natural river discharge is highly regulated by the hydraulic structures, and the regulated flow is substantially different from natural inflow characteristics for the use of water resources planning. The natural inflow data are necessarily required for hydrologic analysis and water resources planning. This study aimed to develop an integrated model for more reliable simulation of daily dam inflow. First, a piecewise Kernel-Pareto distribution was used for rainfall simulation model, which can more effectively reproduce the low order moments (e.g. mean and median) as well as the extremes. Second, a Bayesian Markov Chain Monte Carlo scheme was applied for the SAC-SMA rainfall-runoff model that is able to quantitatively assess uncertainties associated with model parameters. It was confirmed that the proposed modeling scheme is capable of reproducing the underlying statistical properties of discharge, and can be further used to provide a set of plausible scenarios for water budget analysis in water resources planning.

• Journal of Korea Water Resources Association
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• v.50 no.12
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• pp.849-862
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• 2017

The increased frequency of meteorological disasters has been observed due to increased extreme events such as heavy rainfalls and flash floods. Numerous studies using high-resolution weather radar rainfall data have been carried out on the hydrological effects. In this study, a conditional merging technique is employed, which makes use of geostatistical methods to extract the optimal information from the observed data. In this context, three different techniques such as kriging, inverse distance weighting and spline interpolation methods are applied to conditionally merge radar and ground rainfall data. The results show that the estimated rainfall not only reproduce the spatial pattern of sub-hourly rainfall with a relatively small error, but also provide reliable temporal estimates of radar rainfall. The proposed modeling framework provides feasibility of using conditionally merged rainfall estimation at high spatio-temporal resolution in ungauged areas.

• Journal of Korea Water Resources Association
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• v.52 no.11
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• pp.895-904
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• 2019

It has been well recognized that extreme rainfall process often features a nonstationary behavior, which may not be effectively modeled within a stationary frequency modeling framework. Moreover, extreme rainfall events are often described by a two (or more)-component mixture distribution which can be attributed to the distinct rainfall patterns associated with summer monsoons and tropical cyclones. In this perspective, this study explores a Mixture Distribution based Nonstationary Frequency (MDNF) model in a changing rainfall patterns within a Bayesian framework. Subsequently, the MDNF model can effectively account for the time-varying moments (e.g. location parameter) of the Gumbel distribution in a two (or more)-component mixture distribution. The performance of the MDNF model was evaluated by various statistical measures, compared with frequency model based on both stationary and nonstationary mixture distributions. A comparison of the results highlighted that the MDNF model substantially improved the overall performance, confirming the assumption that the extreme rainfall patterns might have a distinct nonstationarity.

• Environmental Engineering Research
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• v.25 no.4
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• pp.545-553
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• 2020

Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.4
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• pp.91-97
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• 2009

The flow is changed by the structure which goes across the river. The structure with debris causes high water level and overflow. The changed flow, which caused by pier and stream characteristics like velocity and slope, was analysed by 2D model. After rainfall, the influences of increased discharge were evaluated. Velocity was simulated in the channel by SMS (Surface water Modeling System) using RMA2, and high velocity values were found in the steep and narrow reach. Highest velocity value around piers was showed in the middle of space between two piers. The increased discharge due to rainfall increases velocity and changes flow contour considerably.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.145-145
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• 2019

Groundwater and rainwater are the only sources of freshwater in small islands as many islands lack surface water sources. Groundwater occurring in the form of freshwater lens floating on denser seawater is highly dependent on natural recharge from rainfall. A sharp interface numerical model for regional and well scale modeling is selected to assess the sustainability of freshwater lens in the island of Tongatapu. In this study, 29 downscaled General Circulation Model(GCM) predictions are input to the recharge model based on water balance modelling. Three GCM predictions which represent wet, dry and medium conditions are selected for use in the groundwater flow model. Total freshwater volume and number of saltwater intruded wells are simulated under various climate scenarios with GCM predicted rainfall pattern, sea level rise and pumping. Simulations indicate that the sustainability of the freshwater lens is threatened by the frequent droughts which are predicted under all scenarios of recharge. The natural depletion of the lens during droughts and increase in water demands, leads to saltwater upconing under the pumping wells. Implementation of drought management measures is of utmost importance to ensure sustainability of freshwater lens in future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1443-1454
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• 2014

In this study, we assess impact of spatial resolution of radar rainfall and a distributed hydrologic model on parameter estimation and rainfall-runoff response. Radar data measured by S-band polarimetric radar located at Mt. Bisl in the year of 2012 are used for the comparative study. As different rainfall estimates such as R-KDP, R-Z, and R-ZDR show good agreement with ground rainfall, R-KDP are applied for rainfall-runoff modeling due to relatively high accuracy in terms of catchment averaged and gauging point rainfall. GRM (grid based rainfall-runoff model) is implemented for flood simulations at the Geumho River catchment with spatial resolutions of 200m, 500m, and 1000m. Automatic calibration is performed by PEST (model independent parameter estimation tool) to find suitable parameters for each spatial resolution. For 200m resolution, multipliers of overlandflow and soil hydraulic conductivity are estimated within stable ranges, while high variations are found from results for 500m and 1000m resolution. No tendency is found in the estimated initial soil moisture. When parameters estimated for different spatial resolution are applied for other resolutions, 200m resolution model shows higher sensitivity compared to 1000m resolution model.

• Journal of Korea Water Resources Association
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• v.51 no.3
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• pp.263-272
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• 2018

For the design of hydraulic structures, the design flood discharge corresponding to a specific frequency is generally used by using the design storm calculated according to the rainfall-runoff relationship. In the past, empirical equations such as rational equations were used to calculate the peak flow rate. However, as the duration of rainfall is prolonged, the outflow patterns are different from the actual events, so the accuracy of the temporal distribution of the probability rainfall becomes important. In the present work, Huff's quartile method is used for the temporal distribution of rainfall, and the third quartile is generally used. The regression equation for Huff's quadratic curve applies a sixth order polynomial equation because of its high accuracy throughout the duration of rainfall. However, in statistical modeling, the regression equation needs to be concise in accordance with the principle of simplicity, and it is necessary to determine the regression coefficient based on the statistical significance level. Therefore, in this study, the statistical significance test for regression equation for temporal distribution of the Huff's quartile method, which is used as the temporal distribution method of design rainfall, is conducted for 69 rainfall observation stations under the jurisdiction of the Korea Meteorological Administration. It is statistically significant that the regression equation of the Huff's quartile method can be considered only up to the 4th order polynomial equation, as the regression coefficient is significant in most of the 69 rainfall observation stations.