• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.2
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• pp.65-71
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• 2008

A monsoon season monitoring data from June to September, 2005 of a small forested watershed located at the upstream of the North Han River system in Korea was conducted to analyze the flow variations, the NPS pollutant concentrations, and the pollution load characteristics with respect to sampling frequencies. During the 4-month period, 1,423 mm or 79.2% of annual rainfall(1,797 mm) were occurred and more than 77%, 54% and 68% of annual T-N, $NO_3$-N and T-P loads discharged. Flow rate was continuously measured with automatic velocity and water level meters and 58 water quality samples were taken and analyzed. It was analyzed that the flow volume by random measurement varied very widely and ranged from 79% to 218% of that of continuous measurement. It was recommended that flow measurement of small forested watersheds should be continuously measured with automated flow meters to precisely measure flow rates. Flow-weighted mean concentrations of T-N, $NO_3$-N and T-P during the period were 2.114 mg/L, 0.836 mg/L, and 0.136 mg/L, respectively. T-N, $NO_3$-N and T-P loads were sensitive to the number of samples. And it was analyzed that in order to measure the pollution load within the error of 10% to the true load, the rate of sampling frequency should be higher than 89.7% of the sample numbers that were required to compute the true pollution load. If it is compared to selected foreign research results, about 10 water samples for each rainfall event were needed to compute the pollution load within 10% error. It is unlikely in Korea and recommended that thorough NPS pollution monitoring studies are required to develop the standard monitoring procedures for reliable NPS pollution quantification.

• Journal of Korea Water Resources Association
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• v.34 no.5
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• pp.473-486
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• 2001

The WGR model was developed to represent meso-scale precipitation. As a conceptual model, this model shows a good link between atmospheric dynamics and statistical description of meso-scale precipitation(Waymire et al., 1984). However, as it has maximum 18 parameters along with its non-linear structure, its parameter estimation has been remained a difficult problem. There have been several cases of its parameter estimation for different fields using non-linear programming techniques(NLP), which were also difficult tasks to hamper its wide applications. In this study, we estimated the WGR model parameters of the Han river basin using the genetic algorithm(GA) and compared them to the NLP results(Yoo and Kwon, 2000). As a result of the study, we can find that the sum of square error from the GA provide more consistent parameters to the seasonal variation of rainfall. Also, we can find that the higher rainfall amount during summer season is closely related with the arrival rate of rain bands, not the rain cell intensity.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.366-366
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• 2012

확률강우량은 하천설계, 수자원설계 및 계획을 위한 기초자료로 활용되며 최근 이상기후 및 기후변화로 인한 극치강우의 빈도 및 양적 증가로 인한 확률강우량 산정의 불확실성 분석에 대한 관심이 크게 증가하고 있다. 수문빈도 해석에 있어서 대부분 지역이 50년 이하의 수문자료가 이용되고 있으며 수문설계에서 요구되는 50년 이상의 확률강수량 추정시에는 상당한 불확실성을 내포하고 있다. 이러한 점에서 본 연구에서는 자료연수에 따른 Sampling Error와 분포형의 매개변수의 불확실성을 고려한 해석모형을 구축하고자 한다. 빈도해석에서 매개변수를 추정하기 위해서는 일반적으로 모멘트법, 최우도법, 확률가중모멘트법이 이용되고 있으나 사용되는 분포형에 따라서 통계학적으로 불확실성 구간을 정량화하는 과정이 난해할 뿐만 아니라 극치 수문자료가 Thick-Tailed분포의 특성을 가짐에도 불구하고 신뢰구간 산정시 정규분포로 가정하는 등 기존 해석 방법에는 많은 문제점을 내포하고 있다. 본 연구에서는 이러한 매개변수의 불확실성 평가에 있어서 우수한 해석능력을 발휘하는 Bayesian기법을 도입하여 분포형의 매개변수를 추정하고 매개변수 추정과 관련된 불확실성을 평가하고자 한다. 이와 별개로 자료연한에 따른 Sampling Error를 추정하기 위해서 Bootstrapping 기반의 해석모형을 구축하고자 하며 최종적으로 빈도해석시에 나타나는 불확실성을 종합적으로 검토하였다. 빈도해석을 위한 확률분포형으로 GEV(generalized extreme value)분포를 이용하였으며 Gibbs 샘플러를 활용한 Bayesian Markov Chain Monte Carlo 모의를 기본 해석모형으로 활용하였다.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.493-505
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• 2017

Maximum likelihood estimation (MLE) of the generalized extreme value distribution (GEVD) is known to sometimes over-estimate the positive value of the shape parameter for the small sample size. The maximum penalized likelihood estimation (MPLE) with Beta penalty function was proposed by some researchers to overcome this problem. But the determination of the hyperparameters (HP) in Beta penalty function is still an issue. This paper presents some data adaptive methods to select the HP of Beta penalty function in the MPLE framework. The idea is to let the data tell us what HP to use. For given data, the optimal HP is obtained from the minimum distance between the MLE and MPLE. A bootstrap-based method is also proposed. These methods are compared with existing approaches. The performance evaluation experiments for GEVD by Monte Carlo simulation show that the proposed methods work well for bias and mean squared error. The methods are applied to Blackstone river data and Korean heavy rainfall data to show better performance over MLE, the method of L-moments estimator, and existing MPLEs.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.2
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• pp.179-185
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• 2016

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

• Journal of Environmental Science International
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• v.18 no.1
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• pp.1-7
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• 2009

In this study a sediment yield is compared by IUSG, IUSG with Kalman filter, tank model and tank model with Kalman filter separately. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. In the IUSG with Kalman filter, the state vector of the watershed sediment yield system is constituted by the IUSG. The initial values of the state vector are assumed as the average of the IUSG values and the initial sediment yield estimated from the average IUSG. A tank model consisting of three tanks was developed for prediction of sediment yield. The sediment yield of each tank was computed by multiplying the total sediment yield by the sediment yield coefficients; the yield was obtained by the product of the runoff of each tank and the sediment concentration in the tank. A tank model with Kalman filter is developed for prediction of sediment yield. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error.

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

• Journal of Korea Water Resources Association
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• v.38 no.5 s.154
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• pp.415-425
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• 2005

This study estimated the optimal rain gauge density and sub-basin size for the application of a daily rainfall-runoff analysis model called SWAT (Soil and Water Assessment Tool). Simulated rainfall data using a WGR multi-dimensional precipitation model (Waymire et al., 1984) were applied to SWAT for runoff estimation, and then the runoff error was analyzed with respect to various rain gauge density and sub-basin size. As results of the study, we could find that the optimal sub-basin size and the representative area of one rain gauge are similar to be about $80km^2$ for the Yong-Dam dam basin.

• Journal of Environmental Science International
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• v.24 no.4
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• pp.525-540
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• 2015

Spatial distribution of precipitation has been estimated based on the local gauge correction (LGC) with a fixed inverse distance weighting (IDW), which is not optimized in taking effective radius into account depending on the radar error. We developed an algorithm, improved local gauge correction (ILGC) which eliminates outlier in radar rainrate errors and optimize distance power for IDW. ILGC was statistically examined the hourly cumulated precipitation from weather for the heavy rain events. Adjusted radar rainfall from ILGC is improved to 50% compared with unadjusted radar rainfall. The accuracy of ILGC is higher to 7% than that of LGC, which resulted from a positive effect of the optimal algorithm on the adjustment of quantitative precipitation estimation from weather radar.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.247-247
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• 2017

수문분석이 있어 정확한 강우량 추정 및 강우 자료의 품질은 매우 중요한 요소이다. 유출분석의 기본 입력 자료인 만큼 홍수유출 결과에도 큰 영향을 미치게 되는데, 현재 하나의 확정적인 값으로 제공되는 레이더 강우 자료는 추정과정에서 많은 오차 및 불확실성을 포함하고 있다. 강우 자료의 불확실성은 기상현상의 예측능력 한계로 인한 것으로 관측지점에서의 발생 가능한 다양한 강우시나리오의 범위를 나타낸다. 본 연구에서는 임의의 값을 추정하는데 있어 하나의 값이 아닌 가능한 값들의 범위를 정의하거나 확률분포를 표현할 수 있는 확률론적인 방법을 이용하여 레이더 강우 앙상블을 생성하고자 하였다. 2012년 남강댐 유역에 발생한 태풍 '산바', '볼라벤'을 대상으로 자료간 오차 공분산을 고려하여 강우 앙상블을 생성하였으며, 레이더 강우에 내포된 불확실성 정도를 정량적으로 제시하였다. 생성된 강우 앙상블은 레이더 강우의 전체적인 편의보정뿐만 아니라 지상강우의 패턴을 잘 모의하고 있는 것으로 나타났으며, 레이더에 의해 추정한 강우의 불확실성을 잘 표현하고 있는 것으로 확인되었다. 강우 앙상블 생성 방법은 발생 가능한 다양한 강우 시나리오를 제공할 수 있으며 홍수예경보와 같은 의사 결정에 유용한 정보를 제공할 수 있을 것으로 판단된다.