• Journal of Korea Water Resources Association
• /
• v.48 no.7
• /
• pp.567-579
• /
• 2015

This study performed the non-stationary flood frequency analysis considering time-varying parameters of a probability density function. Also, return period and risk under non-stationary condition were estimated. A stationary model and three non-stationary models using Generalized Extreme Value(GEV) were developed. The only location parameter was assumed as time-varying parameter in the first model. In second model, the only scale parameter was assumed as time-varying parameter. Finally, the both parameters were assumed as time varying parameter in the last model. Relative likelihood ratio test and Akaike information criterion were used to select appropriate model. The suggested procedure in this study was applied to eight multipurpose dams in South Korea. Using relative likelihood ratio test and Akaike information criterion it is shown that the inflow into the Hapcheon dam and the Seomjingang dam were suitable for non-stationary GEV model but the other six dams were suitable for stationary GEV model. Also, it is shown that the estimated return period under non-stationary condition was shorter than those estimated under stationary condition.

• The Journal of the Korea Contents Association
• /
• v.8 no.3
• /
• pp.252-263
• /
• 2008

This study was conducted to derive the optimal regionalization of the precipitation data which can be classified on the basis of climatologically and geographically homogeneous regions all over the regions except Cheju and Ulreung islands in Korea. A total of 65 rain gauges were used to regional analysis of precipitation. Annual maximum series for the consecutive durations of 1, 3, 6, 12, 24, 36, 48 and 72hr were used for various statistical analyses. K-means clustering mettled is used to identify homogeneous regions all over the regions. Five homogeneous regions for the precipitation were classified by the K-means clustering. Using the L-moment ratios and Kolmogorov-Smirnov test, the underlying regional probability distribution was identified to be the generalized extreme value (GEV) distribution among applied distributions. The regional and at-site parameters of the generalized extreme value distribution were estimated by the linear combination of the probability weighted moments, L-moment. The regional and at-site analysis for the design rainfall were tested by Monte Carlo simulation. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE were computed and compared with those resulting from at-site Monte Carlo simulation. All show that the regional analysis procedure can substantially reduce the RRMSE, RBIAS and RR in RRMSE in the prediction of design rainfall. Consequently, optimal design rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.21 no.3
• /
• pp.241-253
• /
• 2009

This study performs the investigation of a long-term variation of annual maximum surge heights(AMSH) and main characteristics of high surge events, and the statistical evaluation of the AMSH using sea level data at Yeosu and Tongyeong tidal stations over more than 30 years. It is found that the long-term uptrends based on the linear regression in the AMSH are 34.5 cm/34 yr at Yeosu and 33.6 cm/31 yr at Tongyeong, which are relatively much higher than those at Sokcho and Mukho in the Eastern Coast. 71% and 68% of the AMSH occur during typhoon's event in Yeosu and Tongyeong tidal stations, respectively, and the highest surge records are mostly produced by the typhoon. The generalized extreme value distribution taking into account of the time variable is applied to detect time trend in annual maximum surge heights. In addition, Gumbel distribution is checked to find which one is best fitted to the data using likelihood ratio test. The return level and its 90% confidence interval are obtained for the statistical prediction of the future trend. The prevention of the growing storm surge damage by the intensified typhoon requires the steady analysis and prediction of the surge events associated with the climate change.

• Journal of Korean Society of Disaster and Security
• /
• v.13 no.1
• /
• pp.1-11
• /
• 2020

One of the important problem in statistical hydrology is to estimate the appropriated probability distribution for a given sample data. For the problem, a goodness-of-fit test is conducted based on the similarity between estimated probability distribution and assumed theoretical probability distribution. Probability plot correlation coefficient test (PPCC) is one of the goodness-of-fit test method. PPCC has high rejection power and its application is simple. In this study, test statistics of PPCC were derived for generalized extreme value distribution (GEV) models based on L-moments and these statistics were suggested by the multiple and nonlinear regression equations for its usability. To review the rejection power of the newly proposed method in this study, Monte Carlo simulation was performed with other goodness-of-fit tests including the existing PPCC test. The results showed that PPCC-A test which is proposed in this study demonstrated better rejection power than other methods, including the existing PPCC test. It is expected that the new method will be helpful to estimate the appropriate probability distribution model.

• Journal of Korea Water Resources Association
• /
• v.52 no.12
• /
• pp.1001-1009
• /
• 2019

Due to recent increasing heavy snow in Korea, the damage caused by heavy snow is also increasing. In Korea, there are many efforts including establishing disaster prevention measures to reduce the damage throughout the country, but it is difficult to establish the design criteria due to the characteristics of heavy snow. In this study, snowfall frequency analysis was performed to estimate design snow depths using observed snow depth data at Jinju, Changwon and Hapcheon stations. The conventional frequency analysis is sometime limted to apply to the snow depth data containing zero values which produce unrealistc estimates of distributon parameters. To overcome this problem, this study employed mixed distributions based on Lognormal, Generalized Pareto (GP), Generalized Extreme Value (GEV), Gamma, Gumbel and Weibull distribution. The results show that the mixed distributions produced smaller design snow depths than single distributions, which indicated that the mixed distributions are applicable and practical to estimate design snow depths.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.46 no.5
• /
• pp.15-26
• /
• 2004

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation. The optimal regionalization of the precipitation data were classified by the above mentioned regionalization for all over the regions except Jeju and Ulleung islands in Korea. Design rainfalls following the consecutive duration were derived by the regional analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root mean square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE for the design rainfall were computed and compared between the regional and at-site frequency analysis. It has shown that the regional frequency analysis procedure can substantially more reduce the RRMSE, RBIAS and RR in RRMSE than those of at-site analysis in the prediction of design rainfall. Consequently, optimal design rainfalls following the classified regions and consecutive durations were derived by the regional frequency analysis using Generalized extreme value distribution which was identified to be more optimal one than the other applied distributions. Diagrams for the design rainfall derived by the regional frequency analysis using L-moments were drawn according to the regions and consecutive durations by GIS techniques.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.46 no.4
• /
• pp.25-36
• /
• 2004

This study was conducted to derive the design rainfall by the consecutive duration using the at-site frequency analysis. Using the errors, K-S tests and LH-moment ratios, Log Pearson type 3 (LP3) and Generalized Extreme Value (GEV) distributions of Gamma and Non-Gamma Family, respectively were identified as the optimal probability distributions among applied distributions. Parameters of GEV and LP3 distributions were estimated by the method of L and LH-moments and the Indirect method of moments respectively. Design rainfalls following the consecutive duration were derived by at-site frequency analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE) and relative efficiency (RE) in RRMSE for the design rainfall derived by at-site analysis in the observed and simulated data were computed and compared. It has shown that at-site frequency analysis by GEV distribution using L-moments is confirmed as more reliable than that of GEV and LP3 distributions using LH-moments and Indirect method of moments in view of relative efficiency.

• Wind and Structures
• /
• v.30 no.1
• /
• pp.85-97
• /
• 2020

The accurate estimation of the buffeting response of a bridge pylon is related to the quality of the bridge construction. To evaluate the influence of wind field characteristics on the buffeting response of a pylon in a trumpet-shaped mountain pass, this paper deduced a multimodal coupled buffeting frequency domain calculation method for a variable-section bridge tower under the twisted wind profile condition based on quasi-steady theory. Through the long-term measurement of the wind field of the trumpet-shaped mountain pass, the wind characteristics were studied systematically. The effects of the wind characteristics, wind yaw angles, mean wind speeds, and wind profiles on the buffeting response were discussed. The results show that the mean wind characteristics are affected by the terrain and that the wind profile is severely twisted. The optimal fit distribution of the monthly and annual maximum wind speeds is the log-logistic distribution, and the generalized extreme value I distribution may underestimate the return wind speed. The design wind characteristics will overestimate the buffeting response of the pylon. The buffeting response of the pylon is obviously affected by the wind yaw angle and mean wind speed. To accurately estimate the buffeting response of the pylon in an actual construction, it is necessary to consider the twisted effect of the wind profile.

• Wind and Structures
• /
• v.30 no.6
• /
• pp.575-595
• /
• 2020

The characteristics of winds associated with tropical cyclones are of great significance in many engineering fields. This paper presents an investigation of wind characteristics over a coastal urban terrain based on field measurements collected from multiple cup anemometers and ultrasonic anemometers equipped at 13 height levels on a 356-m-high meteorological tower in Shenzhen during severe Typhoon Hato. Several wind quantities, including wind spectrum, gust factor, turbulence intensity and length scale as well as wind profile, are presented and discussed. Specifically, the probability distributions of fluctuating wind speeds are analyzed in connection with the normal distribution and the generalized extreme value distribution. The von Karman spectral model is found to be suitable to depict the energy distributions of three-dimensionally fluctuating winds. Gust factors, turbulence intensity and length scale are determined and discussed. Moreover, this paper presents the wind profiles measured during the typhoon, and a comparative study of the vertical distribution of wind speeds from the field measurements and existing empirical models is performed. The influences of the topography features and wind speeds on the wind profiles were investigated based on the field-measured wind records. In general, the empirical models can provide reasonable predictions for the measured wind speed profiles over a typical coastal urban area during a severe typhoon.

• Wind and Structures
• /
• v.31 no.4
• /
• pp.321-334
• /
• 2020

Engineering type tropical cyclone (TC) wind field models are used to estimate TC wind hazard. Some of the models are well-calibrated using observation data, while others are not extensively compared and verified. They are all proxies to the real TC wind fields. The computational effort for their use differs. In the present study, a comparison of the predicted wind fields is presented by considering three commonly used models: the gradient wind field model, slab-resolving model, and a linear height-resolving model. These models essentially predict the horizontal wind speed at a different height. The gradient wind field model and linear height-resolving model are simple to use while the nonlinear slab-resolving model is more compute-intensive. A set of factors is estimated and recommended such that the estimated TC wind hazard by using these models becomes more consistent. The use of the models, including the developed set of factors, for estimating TC wind hazard over-water and over-land is presented by considering the historical tracks for a few sites. It is shown that the annual maximum TC wind speed can be adequately modelled by the generalized extreme value distribution.