• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.3
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• pp.100-116
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• 1993

It is experienced fact that unreasonable design criterion and unsitable operation management for the agricultural structures including reservoirs based on short terms data of monthly flows have been brought about not only loss of lives, but also enormous property damage. For the solution of this point at issue, this study was conducted to simulate long series of synthetic monthly flows by multi-season first order Markov model with selection of best fitting frequency distribution and to make a comparison of statistical parameters between observed and synthetic flows of six watersheds in Yeong San and Seom Jin river systems. The results obtained through this study can be summarized as follows. 1.Both Gamma and two parameter lognormal distribution were found to be suitable ones for monthly flows in all watersheds by Kolmogorov-Smirnov test while those distributions were judged to be unfitness in Nam Pyeong of Yeong San and Song Jeong and Ab Rog watersheds of Seom Jin river systems in the $\chi$$^2$ goodness of fit test. 2.Most of the arithmetic mean values for synthetic monthly flows simulated by Gamma distribution are much closer to the results of the observed data than those of two parameter lognomal distribution in the applied watersheds. 3.Fluctuation for the coefficient of variation derived by Gamma distribution was shown in general as better agreement with the results of the observed data than that of two parameter lognormal distribution in the applied watersheds both in Yeong San and Seom Jin river systems. Especially, coefficients of variation calculated by Gamma distribution are seemed to be much closer to those of the observed data during July and August. 4.It can be concluded that synthetic monthly flows simulated by Gamma distribution are seemed to be much closer to the observed data than those by two parameter lognormal distribution in the applied watersheds. 5.It is to be desired that multi-season first order Markov model based on Gamma distribution which is confirmed as a good fitting one in this study would be compared with Harmonic synthetic model as a continuation follows.

• Journal of Korea Water Resources Association
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• v.37 no.4
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• pp.329-339
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• 2004

The main objective of this study is to develop a GIS-based two-dimensional model for the simulation of rainfall-runoff process and overland flow of a watershed. The tasks of this study are summarized: to develop a two-dimensional model for overland flow and to construct a rainfall-runoff simulation system linked with GIS. The mathematical formulation of the model incorporates four parts: spatially varied rainfall, spatially distributed infiltration, 1-directional, 4-directional and 8-directional overland flow routing scheme, and one-dimensional channel routing scheme. For the development of stochastic model, Monte Carlo simulation method has been directly integrated into the model. GIS using Arc/Info and ArcView has been applied to prepare the model input data(elevation, soil type, rainfall data, etc.) for a simulation and to demonstrate the simulation results.

• Water for future
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• v.29 no.2
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• pp.179-190
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• 1996

The characteristics of water quality in the downstream of the Han River were analyzed by statistical techniques. Basic characteristics, areal and temporal variations, and correlations of water quality data were investigated. Monthly water quality data have been investigated systematically by exploring data analysis, including time series plot, summary statistics, distribution test, time dependence test, seasonality test and flow relatedness test. Results show that water quality data in this river have seasonality. And applicability of stochastic models such as Thomas-Fiering model and ARMA(1,1) model was identified. From the examination of water quality data related to discharge, it was found that DO and SS are sensitive to water temperature rather than discharge, while BOD and COD are sensitive to discharge at dry seasons. Seasonal periodicities were identified in all water quality variables from the cross correlation analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.403-403
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• 2019

수자원을 효율적으로 관리하고 사용하는 것은 확보한 수자원을 확보한 목표에 맞게 시,공간적으로 적절하게 분배 시키는 것이다. 따라서 저수지 운영의 최종 목표는 댐 건설 목적에 따라 확보된 물을 유입량, 저수량, 용수 수요등을 감안하여 댐 운영 목표에 맞게 최적으로 적절한 양의 물을 적절한 시기에 방류하는 것이다.(손덕환, 2004) 현재 댐군의 운영방법은 확정론적인 방법과 추계학적인 방법이 주로 이용되고 있으나 본 연구에서는 최근 연구가 많이 이루어지고 있는 인공신경망을 적용하여 운영방법으로써의 적용성을 검토하고자한다. 연구대상지로는 수력발전소가 포함된 한강의 충주 다목적댐을 선정하였다. 인공신경망은 입력층에서 출력층사이에 은닉층이 존재하는 다중신경망을 활용하였으며 출력층은 방류량으로 설정하여 발전방류와 수문방류를 구분하여 설정하였다. 방류량 결정을 위한 입력층 구성은 선행 연구들을 참고하여 예측 유입량, 현재 수위, 발전량, 용수 수요량 등을 설정하여 입력층으로 구성하였다. 학습기간의 방류량 자료를 학습하고 검정기간을 통해 실제 이루어진 방류량과 모의된 방류량의 차이를 비교, 분석하여 댐 운영방법으로써의 인공신경망의 적용성을 검토하고자하였다.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1137-1147
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• 2022

River and groundwater stages are the main elements in the hydrologic cycle. They are spatially correlated and can be used to evaluate hydrological and agricultural drought. Stochastic simulation is often performed independently on hydrological variables that are spatiotemporally correlated. In this setting, interdependency across mutual variables may not be maintained. This study proposes the Bayesian vector autoregression model (VAR) to capture the interdependency between multiple variables over time. VAR models systematically consider the lagged stages of each variable and the lagged values of the other variables. Further, an autoregressive model (AR) was built and compared with the VAR model. It was confirmed that the VAR model was more effective in reproducing observed interdependency (or cross-correlation) between river and ground stages, while the AR generally underestimated that of the observed.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.89-102
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• 2010

Future uncertainty on water demand caused by future climate condition and water consumption leads a difficulty to determine the reservoir operation rule for supplying sufficient water to users. It is, thus, important to operate reservoirs not only for distributing enough water to users using the limited water resources but also for preventing floods and drought under the unknown future condition. In this study, the reservoir storage is determined in the first stage when future condition is unknown, and then, water distribution to users and river stream is optimized using the available water resources from the first stage decision using 2-stage stochastic linear programming (2-SLP). The objective function is to minimize the difference between target and actual water storage in reservoirs and the water shortage in users and river stream. Hedging rule defined by a precaution against severe drought by restricting outflow when reservoir storage decreases below a target, is also applied in the reservoir operation rule for improving the model applicability to the real system. The developed model is applied in a system with five reservoirs in the Han River basin, Korea to optimize the multi-reservoir system under various future water demand scenarios. Three multi-purposed dams - Chungju, Hoengseong, and Soyanggang - are considered in the model. Gwangdong and Hwacheon dams are also considered in the system due to the large capacity of the reservoirs, but they are primarily for water supply and power generation, respectively. As a result, the water demand of users and river stream are satisfied in most cases. The reservoirs are operated successfully to store enough water during the wet season for preparing the coming drought and also for reducing downstream flood risk. The developed model can provide an effective guideline of multi-reservoir operation rules in the basin.

• Journal of Korea Water Resources Association
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• v.30 no.5
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• pp.449-457
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• 1997

This study attempts to develop a stochastic-dynamic real-time flow forecasting model for an event-orient watershed storage function model (SFM), which has been used as an official flood computation model in Korea, and to evaluate its performance for real-time flow forecast. The study area is the 747.5$\textrm{km}^2$ Hwecheon basin with outlet at Gaejin and the 8 single flow events during 1983-1986 are selected for comparison and verification of model parameter and model performance. The used model parameters in this study are the same values on field work. It is shown that results from the existing model highly depend on the events, but those from the developed model are stable and well predict the flows for the selected flood events. The coefficient of model efficiency between observed and predicted flows for the events was above 0.90. It is concluded that the developed model that can consider model and observation uncertainties during a flood event is feasible and produces reliable real-time flow forecasts on the area.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.38-49
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• 2012

In this study, we analyze statistical characteristics of influent water quality in Daejeon waste water treatment plant and apply a stochastic model for data generation. In the analysis, the influent water quality data from year 2003 to 2008, except for year 2006, are used. Among water quality variables, we find strong correlations between BOD and T-N; T-N and T-P; BOD and T-P; $COD_{Mn}$ and T-P; and BOD and $COD_{Mn}$. We also find that different water quality variables follow different theoretical probability distribution functions, which also depends on whether the seasonal cycle is removed. Finally, we generate the influent water quality data using the multi-season 1st Markov model (Thomas-Fiering model). With model parameters calibrated for the period 2003~2005, the generated data for 2007~2008 are well compared with observed data showing good agreement in general. BOD and T-N are underestimated by the stochastic model. This is mainly due to the statistical difference in observed data itself between two periods of 2003~2005 and 2007~2008. Therefore, we expect the stochastic model can be applied with more confidence in the case that the data follows stationary pattern.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.399-412
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• 2010

The goal of this research is to apply the neural networks models for the temporal disaggregation of the yearly pan evaporation (PE) data, Republic of Korea. The neural networks models consist of multilayer perceptron neural networks model (MLP-NNM) and generalized regression neural networks model (GRNNM), respectively. And, for the performances evaluation of the neural networks models, they are composed of training and test performances, respectively. The three types of data such as the historic, the generated, and the mixed data are used for the training performance. The only historic data, however, is used for the testing performance. From this research, we evaluate the application of MLP-NNM and GRNNM for the temporal disaggregation of nonlinear time series data. We should, furthermore, construct the credible monthly PE data from the temporal disaggregation of the yearly PE data, and can suggest the available data for the evaluation of irrigation and drainage networks system.

• Journal of Korea Water Resources Association
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• v.34 no.5
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• pp.533-542
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• 2001

In order to establish drought policy, the estimation of drought period for each drought situation should be preceded. Non-linear Water Balance Model(NWBM) and palmer Drought Severity Index (PDSI) can be used for analysis of drought period. As a water balance method considering moisture transfer between land surface and atmosphere, NWBM can be used to estimate transition time between dry and wet period induced by stochastic fluctuations. PDSI is also water balance method to show drought severity comparing actual precipitation with climatically appropriate precipitation based on precipitation and potential evapotranspiration. In this study, the drought periods are estimated using NWBM and PDSI for the Han River Basin. The drought periods according to the soil moisture estimated by NWBS and the drought periods according to drought severity index estimated by PDSI show similar trend. The estimated drought period from extreme drought to wet condition for the Han River Basin is about 3years.