• Journal of the Korean GEO-environmental Society
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• v.16 no.4
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• pp.23-32
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• 2015

This paper addresses the effects of extreme rainfall on the stability of cut slopes in Yen Bai city, Northern Viet Nam. In this area, natural slopes are excavated to create places for infrastructures and buildings. Cut slopes are usually made without proper site investigations; the design is mostly based on experience. In recent years, many slope failures have occurred along these cuts especially in rainy seasons, resulting in properties damaged and loss of lives. To explain the reason that slope failure often happens during rainy seasons, this research analyzed the influence of extreme rainfalls, initial ground conditions, and soil permeability on the changes of pore water pressure within the typical slope, thereafter determining the impact of these changes on the slope stability factor of safety. The extreme rainfalls were selected based on all of the rainfalls triggering landslide events that have occurred over the period from 1960 to 2009. The factor of safety (FS) was calculated using Bishop's simplified method. The results show that when the maximum infiltration capacity of the slope top soil is less than the rainfall intensity, slope failures may occur 14 hours after the rain starts. And when this happens, the rainfall duration is the deciding factor that affects the slope FS values. In short, cut slopes in Yen Bai may be stable in normal conditions after the excavation, but under the influence of tropical rain storms, their stability is always questionable.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.2
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• pp.321-329
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• 2012

In this study, statistical analysis under both stationary and non-stationary climate was conducted for rainfall data measured in Seoul. Generalised Extreme Value (GEV) distribution and Gumbel distribution were used for the analysis. Rainfall changes under the non-stationary climate were estimated by applying time variable (t) to location parameter (${\xi}$). Rainfall depths calculated in non-stationary climate increased by 1.1 to 6.2mm and 1.0 to 4.6mm for the GEV distribution and gumbel distribution respectively from those stationary forms. Changes in annual maximum rainfall were estimated with rate of change in the location parameter (${\xi}1{\cdot}t$), and temporal changes of return period were predicted. This was also available for re-evaluating the current sewer design return period. Design criteria of sewer system was newly suggested considering life expectance of the system as well as temporal changes in the return period.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.655-669
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• 2018

In this study, we developed a hybrid forecasting model based on a four-parameter distribution which allows a simultaneous season-ahead forecasting for both seasonal rainfall and sub-daily rainfall in Han-River and Geum-River basins. The proposed model is mainly utilized a set of time-varying predictors and the associated model parameters were estimated within a Bayesian nonstationary rainfall frequency framework. The hybrid forecasting model was validated through an cross-validatory experiment using the recent rainfall events during 2014~2017 in both basins. The seasonal precipitation results showed a good agreement with the observations, which is about 86.3% and 98.9% in Han-River basin and Geum-River basin, respectively. Similarly, for the extreme rainfalls at sub-daily scale, the results showed a good correspondence between the observed and simulated rainfalls with a range of 65.9~99.7%. Therefore, it can be concluded that the proposed model could be used to better consider climate variability at multiple time scales.

• Journal of Korea Water Resources Association
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• v.50 no.3
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• pp.211-221
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• 2017

In statistical hydrology, various extreme distributions such as the generalized extreme value (GEV), generalized logistic (GLO) and Gumbel (GUM) models have been widely used to analyze the extreme events. In the case of rainfall events in South Korea, the GEV and Gumbel distributions are known to be appropriate among various extreme distribution models. However, the proper probability distribution model may be different depending on the type of extreme events, rainfall duration, region, and statistical characteristics of extreme events. In this regard, it is necessary to apply a wide range of statistical properties that can be represented by the distribution model because it has two shape parameters. In this study, the statistical applicability of rainfall data is analyzed using the Burr XII distribution and the dimensionless L-moment ratio for 620 stations in South Korea. For this purpose, L-skewness and L-kurtosis of the Burr XII distribution are derived and L-moment ratio diagram is drawn and then the applicability of 620 stations was analyzed. As a result, it is found that the Burr XII distribution for the stations of the Han River basin in which L-skewness is relatively larger than L-kurtosis is appropriate, It is possibility of replacing the distribution of commonly used Gumbel or GEV distributions. Therefore, the Burr XII model can be replaced as an appropriate probability model in this basin.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.1
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• pp.41-49
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• 2016

In this study, rainfall characteristics with stationary and non-stationary perspectives were analyzed using generalized extreme value (GEV) distribution and Gumbel distribution models with rainfall data collected in major cities of Korea to reevaluate the return period of sewer flooding in those cities. As a result, the probable rainfall for GEV and Gumbel distribution in non-stationary state both increased with time(t), compared to the stationary probable rainfall. Considering the reliability of ${\xi}_1$, a variable reflecting the increase of storm events due to climate change, the reliability of the rainfall duration for Seoul, Daegu, and Gwangju in the GEV distribution was over 90%, indicating that the probability of rainfall increase was high. As for the Gumbel distribution, Wonju, Daegu, and Gwangju showed the higher reliability while Daejeon showed the lower reliability than the other cities. In addition, application of the maximum annual rainfall change rate (${\xi}_1{\cdot}t$) to the location parameter made possible the prediction of return period by time, therefore leading to the evaluation of design recurrence interval.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.511-514
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• 2003

This study was conducted to estimate the design rainfall by the determination of best fitting order for Higher Probability Weighted Moments of the annual maximum series according to consecutive duration at sixty-five rainfall stations in Korea. Design rainfalls were obtained by generalized extreme value distribution which was selected to be suitable distribution in 4 applied distributions and by L, L1, L2, L3 and L4-moment. The best fitting order for Higher Probability Weighted Moments was determined with the confidence analysis of estimated design rainfall.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.16-16
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• 2011

Statistics of extreme rainfall play a vital role in engineering practice from the perspective of mitigation and protection of infrastructure and human life from flooding. While flood frequency assessments, based on river flood flow data are preferred, the analysis of rainfall data is often more convenient due to the finer spatial nature of rainfall recording networks, often with longer records, and potentially more easily transferable from site to site. The rainfall frequency analysis as a design tool has developed over the years in New Zealand from Seelye's daily rainfall frequency maps in 1947 to Thompson's web based tool in 2010. This paper will present a history of the development of New Zealand rainfall frequency analysis methods, and the details of the latest method, so that comparisons may in future be made with the development of Korean methods. One of the main findings in the development of methods was new knowledge on the distribution of New Zealand rainfall extremes. The High Intensity Rainfall Design System (HIRDS.V3) method (Thompson, 2011) is based upon a regional rainfall frequency analysis with the following assumptions: $\bullet$ An "index flood" rainfall regional frequency method, using the median annual maximum rainfall as the indexing variable. $\bullet$ A regional dimensionless growth curve based on the Generalised Extreme Value (GEV), and using goodness of fit test for the GEV, Gumbel (EV1), and Generalised Logistic (GLO) distributions. $\bullet$ Mapping of median annual maximum rainfall and parameters of the regional growth curves, using thin-plate smoothing splines, a $2km\times2km$ grid, L moments statistics, 10 durations from 10 minutes to 72 hours, and a maximum Average Recurrence Interval of 100 years.

• Journal of Korea Water Resources Association
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• v.45 no.3
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• pp.275-290
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• 2012

In this study, we have developed an optimal time distribution model through extraction of peaks over threshold (POT) series. The median values for annual maximum rainfall dataset, which are obtained from the magnetic recording (MMR) and the automatic weather system(AWS) data at Seoul meteorological observatory, were used as the POT criteria. We also suggested the improved methodology for the time distribution of extreme rainfall compared to Huff method, which is widely used for time distributions of design rainfall. The Huff method did not consider changing in the shape of time distribution for each rainfall durations and rainfall criteria as total amount of rainfall for each rainfall events. This study have suggested an extracting methodology for rainfall events in each quartile based on interquartile range (IQR) matrix and selection for the mode quartile storm to determine the ranking cosidering weighting factors on minutely observation data. Finally, the optimal time distribution model in each rainfall duration was derived considering both data size and characteristics of distribution using kernel density function in extracted dimensionless unit rainfall hyetograph.

• Journal of Korea Water Resources Association
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• v.45 no.4
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• pp.393-407
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• 2012

This study suggests the results of temporal and spatial variations for rainfall data in the Korean Peninsula. We got the index of the rainfall amount, frequency and extreme indices from 65 weather stations. The results could be easily understood by drawing the graph, and the Mann-Kendall trend analysis was also used to determine the tendency (up & downward/no trend) of rainfall and temperature where the trend could not be clear. Moreover, by using the FARD, frequency probability rainfalls could be calculated for 100 and 200 years and then compared each other value through the moment method, maximum likelihood method and probability weighted moments. The Average Rainfall Index (ARI) which is meant comprehensive rainfalls risk for the flood could be obtained from calculating an arithmetic mean of the RI for Amount (RIA), RI for Extreme (RIE), and RI for Frequency (RIF) and as well as the characteristics of rainfalls have been mainly classified into Amount, Extremes, and Frequency. As a result, these each Average Rainfall Indices could be increased respectively into 22.3%, 26.2%, and 5.1% for a recent decade. Since this study showed the recent climate change trend in detail, it will be useful data for the research of climate change adaptation.

• Journal of Korea Water Resources Association
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• v.52 no.11
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• pp.895-904
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• 2019

It has been well recognized that extreme rainfall process often features a nonstationary behavior, which may not be effectively modeled within a stationary frequency modeling framework. Moreover, extreme rainfall events are often described by a two (or more)-component mixture distribution which can be attributed to the distinct rainfall patterns associated with summer monsoons and tropical cyclones. In this perspective, this study explores a Mixture Distribution based Nonstationary Frequency (MDNF) model in a changing rainfall patterns within a Bayesian framework. Subsequently, the MDNF model can effectively account for the time-varying moments (e.g. location parameter) of the Gumbel distribution in a two (or more)-component mixture distribution. The performance of the MDNF model was evaluated by various statistical measures, compared with frequency model based on both stationary and nonstationary mixture distributions. A comparison of the results highlighted that the MDNF model substantially improved the overall performance, confirming the assumption that the extreme rainfall patterns might have a distinct nonstationarity.