• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.595-598
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• 2004

This research seeks to derive the design rainfalls through the L-moment with the test of homogeneity, independence and outlier of data on annual maximum daily rainfall at 38 rainfall stations in Korea. To select the appropriate distribution of annual maximum daily rainfall data by the rainfall stations, Generalized Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO) and Pearson Type 3 (PT3) probability distributions were applied and their aptness were judged using an L-moment ratio diagram and the Kolmogorov-Smirnov (K-S) test. Parameters of appropriate distributions were estimated from the observed and simulated annual maximum daily rainfall using Monte Carlo techniques. Design rainfalls were finally derived by GEV distribution, which was proved to be more appropriate than the other distributions.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.3
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• pp.31-42
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• 2004

This research seeks to derive the design rainfalls through the L-moment with the test of homogeneity, independence and outlier of data on annual maximum daily rainfall at 38 rainfall stations in Korea. To select the appropriate distribution of annual maximum daily rainfall data by the rainfall stations, Generalized Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO) and Pearson Type 3 (PT3) probability distributions were applied and their aptness were judged using an L-moment ratio diagram and the Kolmogorov-Smirnov (K-S) test. Parameters of appropriate distributions were estimated from the observed and simulated annual maximum daily rainfall using Monte Carlo techniques. Design rainfalls were finally derived by GEV distribution, which was proved to be more appropriate than the other distributions.

• Journal of Environmental Science International
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• v.28 no.1
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• pp.19-35
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• 2019

This study investigates the performance of four Bayesian methods, Random Walk Metropolis (RWM), Hit-And-Run Metropolis (HARM), Adaptive Mixture Metropolis (AMM), and Population Monte Carlo (PMC), for estimating the parameters and uncertainties of probability rainfall distribution, and the results are compared with those of conventional parameter estimation methods; namely, the Method Of Moment (MOM), Maximum Likelihood Method (MLM), and Probability Weighted Method (PWM). As a result, Bayesian methods yield similar or slightly better results in parameter estimations compared with conventional methods. In particular, PMC can reduce parameter uncertainty greatly compared with RWM, HARM, and AMM methods although the Bayesian methods produce similar results in parameter estimations. Overall, the Bayesian methods produce better accuracy for scale parameters compared with the conventional methods and this characteristic improves the accuracy of probability rainfall. Therefore, Bayesian methods can be effective tools for estimating the parameters and uncertainties of probability rainfall distribution in hydrological practices, flood risk assessment, and decision-making support.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.757-767
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• 2010

This study employs the KED method using the correlations between probability rainfall and topographical factors as single auxiliary variable for assessing the effectiveness of external variables to improve the reliability in the estimation of spatial distribution of probability rainfall. As a result, the KED method gives similar results compared with deterministic spatial interpolation methods and kriging methods in the estimation of rainfall spatial distribution and mean areal rainfall, and as a result of the cross-validations of KED and kriging methods, the KED method using terrain elevation as auxiliary variable gives the best results, which are not significantly different in comparisons with other methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.2
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• pp.47-55
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• 2022

The objective of this study was to analyze the effect of the duration and time distribution of probability rainfall on farmland inundation for the paddy fields in the drainage improvement project site. In this study, eight drainage improvement project sites were selected for inundation modeling. Hourly rainfall data were collected, and 20- and 30-year frequency probability rainfalls were estimated for 14 different durations. Probability rainfalls were distributed using Intensity-Duration-Frequency (IDF) and Huff time distribution methods. Design floods were calculated for 48 hr and critical duration, and IDF time distribution and Huff time distribution were used for 48 hr duration and critical duration, respectively. Inundation modeling was carried out for each study district using 48 hr and critical duration rainfalls. The result showed that six of the eight districts had a larger flood discharge using the method of applying critical duration and Huff distribution. The results of inundation depth analysis showed similar trends to those of design flood calculations. However, the inundation durations showed different tendencies from the inundation depth. The IDF time distribution is a distribution in which most of the rainfall is concentrated at the beginning of rainfall, and the theoretical background is unclear. It is considered desirable to apply critical duration and Huff time distribution to agricultural production infrastructure design standards in consideration of uniformity with other design standards such as flood calculation standard guidelines.

• Journal of Korea Water Resources Association
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• v.39 no.6 s.167
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• pp.521-531
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• 2006

The formula was proposed through the examination of probability rainfall intensity formula used in Incheon based upon recent occurrences of heavy rain and extraordinary storms. Random-time maximum annual rainfalls were estimated for durations from ten minutes to twenty-four hours from the data by Korea Meteorological Administration. Eleven types of probability distribution are considered to estimate probable rainfall depths for different storm durations at Incheon city. Three goodness-of-fit tests including Chi-square, Kolmogorov-Smirmov and framer Von Misses were used to analyze the tendency of recent rainfall. Considering maximum rainfall occurred, General Extreme Value(GEV) distribution was chosen as the appropriate probability distribution. Five types of probability rainfall formulas including Talbot type, Sherman type, Japanese type, unified type I and unified type II are considered to determine the best type for rainfall intensity at Incheon. The formula was determined considering the time of concentration of sewer system and river at Incheon city. Unified type I was chosen for its accuracy and was proposed to represent rainfall intensity of Incheon district.

• Journal of Korea Water Resources Association
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• v.32 no.4
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• pp.403-415
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• 1999

This study is to derive the rainfall intensity formula based on the representative probability distribution in Korea. The 11 probability distributions which has been widely used in hydrologic frequency analysis are applied to the annual maximum rainfall. The parameters of each probability distribution are estimated by method of moments, maximum likelihood method and method of probability weighted moments. Four tests such as $x^2$-test, Kolmogorv-Smirnov test, difference test and modified difference test are used to determine the goodness of fit of the distributions. The homogeneous tests (Mann-Whitney U test, Kruskal-Wallis one-way analysis of variance of nonparametric test) are applied to find the stations with rainfall homogeneity. The results of homogeneous tests show that there is no representative appropriate distribution for the whole duration in Korea. The whole region could be divided into five zones for 12-durations. The representative probability distribution of each divided zone for 12-durations was determined. The GEV distribution for I,II,V zones and the 3-parameter Weibull distribution for III,IV zones were determined as the representative probability distribution. The rainfall were obtained from representative probability distribution for the selected return periods. Rainfall intensity formula was determined by linearization technique for the rainfall.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.101-110
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• 2017

The National Emergency Management Agency (NEMA) presented the disaster prevention performance target rainfall (DPPTR) for disaster prevention. The estimation criteria for DPPTR is a 10 year cycle. On the other hand, the target rainfall recalculated every 10 years is difficult to reflect the current change in rainfall on climate change. In this study, the probability of precipitation using the recent rainfall data was prepared and the weights according to socio-economic criteria reflecting the urban characteristics and adjusted probability rainfall criteria were applied to the results. The difference between the existing target rainfall and recalculated result was compared. The input data for the estimated probability rainfall was selected from 6 points located in the rainfall observing station of Chungcheongnam-do, Daejeon region. As a result of the estimation, in the case of upward probability precipitation weight, some similar areas were observed. On the other hand, there were a few cases of upward or downward changes within 10 mm. Considering the rainfall variability and uncertainty due to climate change, the existing target rainfall does not present the condition properly. Therefore, hydrological designers need to calculate the target rainfall, reflecting the present condition.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.177-189
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• 2022

The purpose of this study is to apply the d4PDF (Data for Policy Decision Making for Future Change) constructed from a large-scale ensemble climate simulation to estimate the probable rainfall with low frequency and high intensity. In addition, this study analyzes the uncertainty caused by the application of the frequency analysis by comparing the probable rainfall estimated using the d4PDF with that estimated using the observed data and frequency analysis at Geunsam, Imsil, Jeonju, and Jangsu stations. The d4PDF data consists of a total of 50 ensembles, and one ensemble provides climate and weather data for 60 years such as rainfall and temperature. Thus, it was possible to collect 3,000 annual maximum daily rainfall for each station. By using these characteristics, this study does not apply the frequency analysis for estimating the probability rainfall, and we estimated the probability rainfall with a return period of 10 to 1000 years by distributing 3,000 rainfall by the magnitude based on a non-parametric approach. Then, the estimated probability rainfall using d4PDF was compared with those estimated using the Gumbel or GEV distribution and the observed rainfall, and the deviation between two probability rainfall was estimated. As a result, this deviation increased as the difference between the return period and the observation period increased. Meanwhile, the d4PDF reasonably suggested the probability rainfall with a low frequency and high intensity by minimizing the uncertainty occurred by applying the frequency analysis and the observed data with the short data period.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.228-232
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• 2017

Extreme hydrological events can cause serious threats to the society. Hence, the selection of probability distributions for extreme rainfall is a fundamental issue. For this reason, this study was focused on understanding possible distributional changes in annual daily maximum rainfalls (AMRs) over time in Sri Lanka using quantile regression. A simplified nine-category distributional-change scheme based on comparing empirical probability density function of two years (i.e. the first year and the last year), was used to determine the distributional changes in AMRs. Daily rainfall series of 13 station over Sri Lanka were analyzed for the period of 1960-2015. 4 distributional change categories were identified for the AMRs. 5 stations showed an upward trend in all the quantiles (i.e. 9 quantiles: from 0.05 to 0.95 with an increment of 0.01 for the AMR) which could give high probability of extreme rainfall. On the other hand, 8 stations showed a downward trend in all the quantiles which could lead to high probability of the low rainfall. Further, we identified a considerable spatial diversity in distributional changes of AMRs over Sri Lanka.