• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.827-837
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• 2012

The estimation of the rainfall quantile is of great importance in designing hydrologic structures. Conventionally, the rainfall quantile is estimated by univariate frequency analysis with an appropriate probability distribution. There is a limitation in which duration of rainfall is restrictive. To overcome this limitation, bivariate frequency analysis by using 3 copula models is performed in this study. Annual maximum rainfall events in 5 stations are used for frequency analysis and rainfall depth and duration are used as random variables. Gumbel (GUM), generalized logistic (GLO) distributions are applied for rainfall depth and generalized extreme value (GEV), GUM, GLO distributions are applied for rainfall duration. Copula models used in this study are Frank, Joe, and Gumbel-Hougaard models. Maximum pseudo-likelihood estimation method is used to estimate the parameter of copula, and the method of probability weighted moments is used to estimate the parameters of marginal distributions. Rainfall quantile from this procedure is compared with various marginal distributions and copula models. As a result, in change of marginal distribution, distribution of duration does not significantly affect on rainfall quantile. There are slight differences depending on the distribution of rainfall depth. In the case which the marginal distribution of rainfall depth is GUM, there is more significantly increasing along the return period than GLO. Comparing with rainfall quantiles from each copula model, Joe and Gumbel-Hougaard models show similar trend while Frank model shows rapidly increasing trend with increment of return period.

• The Journal of Engineering Geology
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• v.16 no.2 s.48
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• pp.201-214
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• 2006

To study the relationship between rainfall conditions and landslides according to a geological condition in land-slides areas such asJangheung Kyounggi, Sangju and Pohang Kyoungbuk, the data of rainfall and landslides are investigated and analyzed. Many landslides occurred at these areas because of the heavy rainfall in two or four days of the summer 1998. The data of rainfall are collected in observatories within a 50km radius from landslides occurrence areas, and the data of landslides are investigated directly in landslides areas. The data of rainfall are the accumulative rainfall and the rainfall intensity, and the data of landslides are the occurrence frequency considering the geological condition. These data are analyzed statistically to know the relationship the rainfall and landslides. The landslides are concentrated in the heavy rainfall area from the analysis of these data. It knows that the land-slides are triggered by the heavy rainfall. Meanwhile, the rainfall factors such as the accumulative rainfall, the rain-fall intensity and the dropping time are different in each landslides area, and the shape and frequency of landslides are different respectively. The landslides have occurred in the area of high accumulative rainfall, while the land-slides have not occurred around that area. Therefore, the rainfall is very important factor induced by the landslides, and the accumulative rainfall is really related to the frequency of landslides.

• 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 the Korean Society of Hazard Mitigation
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• v.1 no.3 s.3
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• pp.111-122
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• 2001

A reliability analysis is conducted on the process in estimating frequency rainfalls. 39 year of annual maximum data in Seoul station are collected to analyze the reliability in the frequency analysis technique. Frequency analysis is carried out for the nine types of distribution function and three types of parameter estimation method which are currently used in Korea. Reliability Analysis is conducted for the frequency rainfalls determined by 3 types(maximum, median, selected) of method to select the adequate rainfall. Computed rainfalls of each duration and return period are standardized to find the distribution of the frequency rainfall. It shows that the resulting rainfall distribution fairly represents the normal distribution. Coefficient of variation of 0.0456 is computed to be used in estimating the reliability based design rainfall.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.367-370
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• 2007

In recent, the utilization of underground in urban area is increased as subway, store, and others but we have done a few studies for the inundation of underground due to flooding. So, we investigate the examples of underground inundation and the causes of inundation for flood events. And the relationship between flood characteristics and underground inundation is analyzed through the frequency of rainfall which is made by annual multi maximum series. As a result, most of underground is inundated by high frequency of rainfall. And there are some cases that the underground is inundated by low frequency of rainfall because of poor drainage system and characteristics of location.

• Journal of Environmental Science International
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• v.29 no.9
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• pp.871-883
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• 2020

Due to recent heavy rain events, there are increasing demands for adapting infrastructure design, including drainage facilities in urban basins. Therefore, a clear definition of urban rainfall must be provided; however, currently, such a definition is unavailable. In this study, urban rainfall is defined as a rainfall event that has the potential to cause water-related disasters such as floods and landslides in urban areas. Moreover, based on design rainfall, these disasters are defined as those that causes excess design flooding due to certain rainfall events. These heavy rain scenarios require that the design of various urban rainfall facilities consider design rainfall in the target years of their life cycle, for disaster prevention. The average frequency of heavy rain in each region, inland and coastal areas, was analyzed through a frequency analysis of the highest annual rainfall in the past year. The potential change in future rainfall intensity changes the service level of the infrastructure related to hand-to-hand construction; therefore, the target year and design rainfall considering the climate change premium were presented. Finally, the change in dimensional safety according to the RCP8.5 climate change scenario was predicted.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.367-377
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• 2010

In this study, we considered annual maximum rainfall data from 56 weather stations for rainfall frequency analysis using SIR(Sampling Important Resampling) algorithm and Bootstrap method. SIR algorithm is resampling method considering weight in extreme rainfall sample and Bootstrap method is resampling method without considering weight in rainfall sample. Therefore we can consider the difference between SIR and Bootstrap method may be due to the climate change. After the frequency analysis, we compared the results. Then we derived the results which the frequency based rainfall obtained using the data from SIR algorithm has the values of -10%~60% of the rainfall obtained using the data from Bootstrap method.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.51-60
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• 2019

In this study, we performed At-site Frequency Analysis(AFA) and Regional Frequency Analysis(RFA) using the observed and climate change scenario data, and the relative root mean squared error(RMMSE) was compared and analyzed for both approaches through Monte Carlo simulation. To evaluate the rainfall quantile, the daily rainfall data were extracted for 615 points in Korea from HadGEM3-RA(12.5km) climate model data, one of the RCM(Regional Climate Model) data provided by the Korea Meteorological Administration(KMA). Quantile mapping(QM) and inverse distance squared methods(IDSM) were applied for bias correction and spatial disaggregation. As a result, it is shown that the RFA estimates more accurate rainfall quantile than AFA, and it is expected that the RFA could be reasonable when estimating the rainfall quantile based on climate change scenarios.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.5
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• pp.89-97
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• 2007

This study proposed new simple methodologies for testing the stationarity of rainfall quantiles, and applied to the rainfall data at Seoul. The methodologies in this study are based on the analysis of frequency change of rainfall quantiles, different from previous studies like Ahn et al. (2001) who analyzed the change of rainfall quantiles themselves. The different types of methodologies are proposed in this study; one is to evaluate the occurrence frequency of rainfall with its return period more than the data length, and the other is to evaluate the effect of new observation on the highest rainfall data recorded. The application of these methodologies shows that the rainfall quantiles at Seoul have no significant proof leading their non-stationarity.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.249-259
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• 2018

More than half of annual rainfall occurs in summer season in Korea due to its climate condition and geographical location. A frequency analysis is mostly adopted for designing hydraulic structure under the such concentrated rainfall condition. Among the various distributions, univariate Gumbel distribution has been routinely used for rainfall frequency analysis in Korea. However, the distributional changes in extreme rainfall have been globally observed including Korea. More specifically, the univariate Gumbel distribution based rainfall frequency analysis is often fail to describe multimodal behaviors which are mainly influenced by distinct climate conditions during the wet season. In this context, we purposed a Gumbel mixture distribution based rainfall frequency analysis with a Bayesian framework, and further the results were compared to that of the univariate. It was found that the proposed model showed better performance in describing underlying distributions, leading to the lower Bayesian information criterion (BIC) values. The mixed Gumbel distribution was more robust for describing the upper tail of the distribution which playes a crucial role in estimating more reliable estimates of design rainfall uncertainty occurred by peak of upper tail than single Gumbel distribution. Therefore, it can be concluded that the mixed Gumbel distribution is more compatible for extreme frequency analysis rainfall data with two or more peaks on its distribution.