• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1798-1802
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• 2007

The practice of business estimate flood discharge by rainfall-flow relation that is easy collection of observation data. The important factor is rainfall, coefficient of runoff, and drainage area for analysis of runoff-flow relation.The practice of business usually use probability rainfall that use a weighted average value after each observation post estimate probability of non-same time. It has more error than same time probability rainfall, and it can excess of estimation because it can't consider space distribution of rainfall.The study of result showed similar aspect with existing ARF but width of coefficient become smaller. And the comparison of peak flow did not different what used by ARF and same time probability rainfall(A group). But non-same time probability rainfall is bigger 25% more than another(B group). Between A group and B group of the difference increased with the lapse of time.

• Journal of Industrial Technology
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• v.27 no.A
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• pp.95-102
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• 2007

The practice of business estimate flood discharge by rainfall-flow relation that is easy collection of observation data. The important factor is rainfall, coefficient of runoff, and drainage area for analysis of runoff-flow relation. The practice of business usually use probability rainfall that use a weighted average value after each observation post estimate probability of non-same time. It has more error than same time probability rainfall, and it can excess of estimation because it can't consider space distribution of rainfall. The study of result showed similar aspect with existing ARF but width of coefficient become smaller. And the comparison of peak flow did not different what used by ARF and same time probability rainfall(A group). But non-same time probability rainfall is bigger 25% more than another(B group). Between A group and B group of the difference increased with the lapse of time.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.6
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• pp.75-83
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• 2010

The objective of this paper is to study the discharge characteristics of pollutant loads in small watershed according to probability rainfall using the Hydrologic Simulation Program-Fortran (WinHSPF). The subwatershed of Gosam reservoir watershed in Gyeonggido province was simulated and the probability rainfall of study area was estimated by recurrence interval and duration. The probability rainfalls are 156.5, 205.9 and 277.4 mm for 6 hrs, 12 hrs and 24 hrs in 10 year frequency, and each probability rainfalls is distributed by Huff's 4th quantiles method and applied to HSPF. The pollutant loads were high for initial rainfall. The concentrations of TN, TP and BOD were high as rainfall duration is shorter and rainfall intensity is higher.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.329-340
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• 2011

The probability concepts mainly used for rainfall or flood frequency analysis in water resources planning are the frequentist viewpoint that defines the probability as the limit of relative frequency, and the unknown parameters in probability model are considered as fixed constant numbers. Thus the probability is objective and the parameters have fixed values so that it is very difficult to specify probabilistically the uncertianty of these parameters. This study constructs the uncertainty evaluation model using Bayesian MCMC and Metropolis -Hastings algorithm for the uncertainty quantification of parameters of probability distribution in rainfall frequency analysis, and then from the application of Bayesian MCMC and Metropolis- Hastings algorithm, the statistical properties and uncertainty intervals of parameters of probability distribution can be quantified in the estimation of probability rainfall so that the basis for the framework configuration can be provided that can specify the uncertainty and risk in flood risk assessment and decision-making process.

• Journal of Korea Water Resources Association
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• v.51 no.7
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• pp.599-606
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• 2018

Most hydrological analysis such as probability rainfall and rainfall time distributions have typically carried out based on hourly rainfall and rainfall - runoff analysis have carried out by applying different periods of rainfall time distribution and probability rainfall. In this study, to quantify the change of design flood due to the data type (hourly and minutely rainfall data) and the probability rainfall and application of different data period to the rainfall time distribution, probability rainfall is calculated by point frequency analysis according to data type and period and rainfall time distribution was calculated by Huff's quartile distributions. In addition, the change analysis of design flood was carried out by rainfall - runoff analysis applying different data periods of design rainfall time distribution. and probability rainfall. As a result, rainfall analysis using minute rainfall data was more accurate and effective than using hourly rainfall data. And the design flood calculated by applying different data period of rainfall time distribution and probability rainfall made a large difference than by applying different data type. It is expected that this will contribute to the hydrological analysis using minutely rainfall.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.187-199
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• 2010

This study evaluated applicability and confidence of probability rainfalls estimated by the non-stationary rainfall frequency analysis which was recently developed. Using rainfall data at 4 sites which have an obvious increasing trend in observations, we estimated 3 type probability rainfalls; probability rainfalls from stationary rainfall frequency analysis using data from 1973-1997, probability rainfalls from stationary rainfall frequency analysis using data from 1973-2006, probability rainfalls from non-stationary rainfall frequency analysis assuming that the current year is 1997 and the target year is 2006. Based on the comparison of residuals from 3 probability rainfalls, the non-stationary rainfall frequency analysis provided more effective and well-directed estimates of probability rainfalls in the target year. Using Bootstrap resampling, this study also evaluated the parameter estimation methods for the non-stationary rainfall frequency analysis based on confidence intervals. The confidence interval length estimated by the maximum likelihood estimation (MLE) is narrower than the probability weighted moments (PWM). The results indicated that MLE provides more proper confidence than PWM for non-stationary probability rainfalls.

• Water for future
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• v.28 no.2
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• pp.145-154
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• 1995

The various analyses of the historical rainfall data need to be utilized in a hydraulic engineering project. The probability distributions of the rainfall events according to annual maximum continuous rainfall depths are studied for the hydrologic frequency analysis. The bivariate normal distribution, the bivariate lognormal distribution, and the bivariate gamma distribution are applied to the rainfall events composed of rainfall depths and its durations at Kangnung, Seoul, Incheon, Chupungnyung, Teagu, Jeonju, Kwangju, and Busan. These rainfall events are fitted to the the bivariate normal distribution and the bivariate lognormal distribution, but not fitted to the bivariate gamma distribution. Frequency curves of probability rainfall events are suggested from the probability distribution selected by the goodness-of-fit test.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.17-27
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• 2020

The objective of this study was to assess the flood probability based on temporal distribution of forecasted-rainfall in Cheongmicheon watershed. In this study, 6-hr rainfalls were disaggregated into hourly rainfall using the Multiplicative Random Cascade (MRC) model, which is a stochastic rainfall time disaggregation model and it was repeated 100 times to make 100 rainfalls for each storm event. The watershed runoff was estimated using the Clark unit hydrograph method with disaggregated rainfall and watershed characteristics. Using the peak discharges of the simulated hydrographs, the probability distribution was determined and parameters were estimated. Using the parameters, the probability density function is shown and the flood probability is calculated by comparing with the design flood of Cheongmicheon watershed. The flood probability results differed for various values of rainfall and rainfall duration. In addition, the flood probability calculated in this study was compared with the actual flood damage in Cheongmicheon watershed (R² = 0.7). Further, this study results could be used for flood forecasting.

• Journal of Wetlands Research
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• v.2 no.1
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• pp.49-58
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• 2000

The frequency analysis of annual maximum rainfall data and the derivation of probable rainfall intensity formula at Masan station are performed in this study. Based on the eight different rainfall duration data from 10 minutes to 24 hours, eight types of probability distribution (Gamma, Lognormal, Log-Pearson type III, GEV, Gumbel, Log-Gumbel, Weibull, and Wakeby distributions), three types of parameter estimation scheme (moment, maximum likelihood and probability weighted methods) and three types of goodness-of-fit test (${\chi}^2$, Kolmogorov-Smirnov and Cramer von Mises tests) were considered to find an appropriate probability distribution at Masan station. The Lognormal-2 distribution was selected and the probable rainfall intensity formula was derived by regression analysis. The derived formula can be used for estimating rainfall quantiles of the Masan vicinity areas with convenience and reliability in practice.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.321-327
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• 2011

Bootstrap methods is the computer-based resampling method that estimates the standard errors and confidence intervals of summary statistics using the plug-in principle for assessing the accuracy or uncertainty of statistical estimates, and the BCa method among the Bootstrap methods is known much superior to other Bootstrap methods in respect of the standards of statistical validation. Therefore this study suggests the method of the representation and treatment of uncertainty in flood risk assessment and water resources planning from the construction and application of rainfall frequency analysis model considersing the uncertainty based on the nonparametric BCa method among the Bootstrap methods for the assessement of the estimation of probability rainfall and the effect of uncertainty considering the uncertainty of the parameter estimation of probability in the rainfall frequency analysis that is the most fundamental in flood risk assessement and water resources planning.