• Proceedings of the Korea Water Resources Association Conference
• /
• 1998.05a
• /
• pp.121-126
• /
• 1998

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.598-602
• /
• 2008

우기시 도시하천으로 유입되는 합류식 관거의 월류수에 의한 오염부하량에는 평상시의 기본 하수량 및 오염부하량이 포함되기 때문에 이에 대한 사전 평가가 필수적이다. 또한, 장기유출에 있어서 건기시 하수유량 및 처리수의 방류량은 중요한 요소로서 현재 실무에서 사용하고 있는 SWMM, STORM 모형에서 기본값으로 제시되고 있는 건기시 유량에 대한 시간 및 일하수량비는 외국의 자료를 바탕으로 한 계수값으로서 이에 대한 검토 및 평가가 필요하다. 따라서, 이 연구에서는 국토해양부 도시홍수재해관리기술연구단에서 운영중인 서울 군자 배수구역의 일년간 건기유량 관측자료를 활용하여 요일별, 시간별 평균유량을 산출하였으며, 그 경향성 분석을 분석하였다. 또한, 기존 모형에서의 기본값들과의 비교를 통하여 그 적정성을 검토하고 국내 자료에 근거한 시간 및 일하수량비를 제시하였다. 시간별 일하수량비의 경우에서는 STORM 모형과는 다소 상이하였으나 SWMM 모형과는 비교적 그 경향이 일치하였다. 관측자료의 분석결과에 의하면 $23{\sim}06$시까지의 시간별 하수량이 매우 적은 것으로 분석되었으며 이는 해당 시간대에 유동인구가 적은 군자 배수구역의 특징을 잘 반영한 것으로 판단된다. 한편, 요일별 일하수량비를 비교한 결과 기존모형의 경우에서 보다 주말기간에는 상대적으로 큰 값을 나타내는 상이한 패턴을 보였다. 이 연구 결과는 다양한 도시 배수구역의 일하수량 경향의 분석을 통하여 도시유출 특성분석과 하수관거의 설계 및 관리에 유용한 자료로 활용될 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 1990.07a
• /
• pp.19-19
• /
• 1990

The paper presents the development and applications of physically-based urban runoff analysis model, URAM, which is capable of simulating sewer runoff hydrographs and inundation conditions within a small urban catchment. The model considers three typical flow conditions of urban drainage networks, whichn are overland flow, gutter flow, and conduit flow during a storm. Infiltration, retention storage and flow routing procedures are physically depicted in model. It was tested satisfactorily with field data from a tested catchment having drainage area of 4.91 ha. It was also applied to other urban areas and found to adequately simulate inundation areas and duration as observed during storms. The test results as well as model components are described in the paper.

• Korean Journal of Ecology and Environment
• /
• v.52 no.1
• /
• pp.21-27
• /
• 2019

Analysis of water quality distribution is very important for river water quality management. Recently, various studies have been conducted on the analysis of water quality distribution according to reduction methods of nonpoint pollutant. The objective of this study was to select the probability distributions of water quality constituents (T-N, T-P, COD, SS) according to the farming forms (control, slow release fertilizer, water depth control) during non-storm period in the paddy fields. The field monitoring was conducted monitoring site located in Baeksan-myun, Buan-gun, Jeollabuk-do, Korea during non-storm period from May to September in 2016. Our results showed that there were no differences in water quality among three different farming forms, except for SS of control and water depth control. K-S method was used to analyzed the probability distributions of T-N, T-P, COD and SS concentrations discharged from paddy fields. As a results of the fitness analysis, T-N was not suitable for the normal probability distribution in the slow release fertilizer treatment, and the log-normal probability distribution was not suitable for the T-P in control treatment. The gamma probability distribution showed that T-N and T-P in control and slow release fertilizer treatment were not suitable. The Weibull probability distribution was found to be suitable for all water quality constituents of control, slow release fertilizer, and water depth control treatments. However, our results presented some differences from previous studies. Therefore, it is necessary to analyze the characteristics of pollutants flowing out in difference periods according to various farming types. The result of this study can help to understand the water quality characteristics of the river.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.6B
• /
• pp.709-721
• /
• 2008

This paper is to test the applicability of ModKIMSTORM (Modified KIneMatic Wave STOrm Runoff Model) by applying it to Namgangdam watershed of $2,293km^2$. Model inputs (DEM, land use, soil related information) were prepared in 500 m spatial resolution. Using five typhoon events (Saomi in 2000, Rusa in 2002, Maemi in 2003, Megi in 2004 and Ewiniar in 2006) and two storm events (May of 2003 and July of 2004), the model was calibrated and verified by comparing the simulated streamflow with the observed one at the outlet of the watershed. The Pearson's coefficient of determination $R^2$, Nash and Sutcliffe model efficiency E, the deviation of runoff volumes $D_v$, relative error of the peak runoff rate $EQ_p$, and absolute error of the time to peak runoff $ET_p$ showed the average value of 0.984, 0.981, 3.63%, 0.003, and 0.48 hr for 4 storms calibration and 0.937, 0.895, 8.08%, 0.138, and 0.73 hr for 3 storms verification respectively. Among the model parameters, the stream Manning's roughness coefficient was the most sensitive for peak runoff and the initial soil moisture content was highly sensitive for runoff volume fitting. We could look into the behavior of hyrologic components from the spatial results during the storm periods and get some clue for the watershed management by storms.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.110-110
• /
• 2016

도시 유역의 강우-유출 모의에는 지표 투수율 및 하수관거 영향 등 인위적 배수계통의 영향을 고려할 수 있는 도시유출모형이 널리 이용되고 있으며, 모형 검증을 통해 모의 성능을 평가한다. 도시유출모형의 검증은 일반적인 강우-유출 모형과 같이 강우사상별 유량의 관측시계열과 모의시계열의 목적함수가 최소가 되는 최적 매개변수를 탐색하는 과정이다. 도시유출모형의 검증에서 발생하는 문제점은 크게 다음과 같다. 첫째, 대규모 도시 유역의 복잡하고 다양한 하수관거에 대한 최적매개변수를 관거별로 구하는 것은 물리적으로 불가능하다. 따라서 동일 배수분구내 하수관거의 매개변수 값은 동일하다고 가정하거나, 모형 단순화 과정을 통해 매개변수의 물리적 범위 내에서 최적해를 탐색해야 하는 단순화에서 기인한 불확실성이 있다. 둘째, 다양한 매개변수들의 물리적 범위를 고려하기 위해서는 전역최적화기법이 유효하다. 그러나 전역최적화 종류, 목적함수, 모의횟수, 목표성능별 최적 매개변수 결과가 각각 다르므로 추정된 최적 매개변수의 범위에 대한 불확실성이 있다. 이에 본 연구에서는 Bayesian 모형과 EPA SWMM(Storm Water Management Model)을 연계하여 도시유출모형의 매개변수 불확실성을 정량적으로 분석할 수 있는 모형을 제안하고자 한다. 이를 위해 서울 우이천 유역을 대상으로 SWMM 모형을 구축하고, 절단 정규분포(truncated Gaussian distribution)를 사전분포(prior)로 가정하여 매개변수의 물리적 범위를 고려하였다. 최종적으로 결합확률분포로 계산된 각 매개변수간 사후분포를 통해 모의된 유출량의 불확실성을 정량적으로 분석하였다. 본 연구에서 제안된 모형은 대규모 도시 유역의 도시유출모형 구축 시 다양한 매개변수의 물리적 범위를 고려한 최적화와 동시에 내재된 불확실성을 정량적으로 분석할 수 있으므로, 침수예측 및 홍수예경보 등의 문제에서 상당한 신뢰성을 확보할 수 있을 것으로 판단된다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.11 no.1
• /
• pp.109-116
• /
• 1991

In recent years, hydrologists have been interested in the radial spectrum and its estimation in two dimensional storm field to construct simulation model of the rainfall. This paper deals with the problem of transformation from the spectrum or isotropic covariance function to two dimensional random field. The extended turning band method for the generation of random field is applied to the problem using the line generation method of one dimensional stochastic process by G.Matheron. Examples of this generation is chosen in the random components of the multidimensional rainfall model suggested by Bras and are given with a comparison between theoretical and sample statistics. In this numerical experiments it is observed that first and second order statistics can be conserved. Also the example of moving storm simulation through Bras model is presented with the appropriate parameters and sample size.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.6B
• /
• pp.697-707
• /
• 2008

The grid-based KIneMatic wave STOrm Runoff Model (KIMSTORM) by Kim (1998) predicts the temporal variation and spatial distribution of overland flow, subsurface flow and stream flow in a watershed. The model programmed with C++ language on Unix operating system adopts single flowpath algorithm for water balance simulation of flow at each grid element. In this study, we attempted to improve the model by converting the code into FORTRAN 90 on MS Windows operating system and named as ModKIMSTORM. The improved functions are the addition of GAML (Green-Ampt & Mein-Larson) infiltration model, control of paddy runoff rate by flow depth and Manning's roughness coefficient, addition of baseflow layer, treatment of both spatial and point rainfall data, development of the pre- and post-processor, and development of automatic model evaluation function using five evaluation criteria (Pearson's coefficient of determination, Nash and Sutcliffe model efficiency, the deviation of runoff volume, relative error of the peak runoff rate, and absolute error of the time to peak runoff). The modified model adopts Shell Sort algorithm to enhance the computational performance. Input data formats are accepted as raster and MS Excel, and model outputs viz. soil moisture, discharge, flow depth and velocity are generated as BSQ, ASCII grid, binary grid and raster formats.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.12 no.2
• /
• pp.123-130
• /
• 1992

We check up on whether the one-dimensional station precipitation model of Geogakakos and Bras is suitable to the storm model for Chonju station or not. The fundamental variables of the physically based model consists of the pressure at the cloud top, the hight-averaged updraft velocity(HAUV), and the inverse of the average diameter of the hydrometeors(ADH) at cloud base. And they are parameterized by input variables. The parameters are eastimated by the direct search algorithm of Hooke and Jeeves in this paper. The results show that HAUV and ADH are dominant factors to minimize root mean square error between the calculated and the observed rainfall. In this numerical analysis, the deviation between the calculated and the total observed rainfall is small, otherwise the gap for the time distribution is quite big.

• Water for future
• /
• v.28 no.5
• /
• pp.219-233
• /
• 1995

A non-stationary multivariate model is selected in which the mean and variance of rainfall are not temporally or spatially constant. And the rainfall prediction system is constructed which uses the recursive estimation algorithm, Kalman filter, to estimate system states and parameters of rainfall model simulataneously. The on-line, real-time, multivariate short-term, rainfall prediction for multi-stations and lead-times is carried out through the estimation of non-stationary mean and variance by the storm counter method, the normalized residual covariance and rainfall speed. The results of rainfall prediction system model agree with those generated by non-stationary multivariate model. The longer the lead time is, the larger the root mean square error becomes and the further the model efficiency decreases form 1. Thus, the accuracy of the rainfall prediction decreases as the lead time gets longer. Also it shows that the mean obtained by storm counter method constitutes the most significant part of the rainfall structure.