Woosung Nam

Regional Rainfall Frequency Analysis by Multivariate Techniques

Regional rainfall quantile depends on the identification of hydrologically homogeneous regions. Various variables relevant to precipitation can be used to form regions. Since the type and number of variables may lead to improve the efficiency of partitioning, it is important to select those precipitation related variables, which represent most of the information from all candidate variables. Multivariate analysis techniques can be used for this purpose. Procrustes analysis which can decrease the dimension of variables based on their correlations, are applied in this study. 42 rainfall related variables are decreased into 21 ones by Procrustes analysis. Factor analysis is applied to those selected variables and then 5 factors are extracted. Fuzzy-c means technique classifies 68 stations into 6 regions. As a result, the GEV distributions are fitted to 6 regions while the lognormal and generalized logistic distributions are fitted to 5 regions. For the comparison purpose with previous results, rainfall quantiles based on generalized logistic distribution are estimated by at-site frequency analysis, index flood method, and regional shape estimation method.

Regional Frequency Analysis of South Korean Rainfall Data Using FORGEX Method

Rainfall quantiles were estimated by applying the FORGEX method. The circle network and two elliptical ones with the ratios of 1 to 1.5 and 1 to 2.0 were used and compared to find appropriate one for rainfall data. Annual maximum data were collected from 376 sites and standardized by the median. The networks were organized from the subject sites and then pooled and netmax data were collected from each network. Then, the growth curves and quantiles were estimated. When the subject site had small differences of quantiles from index flood method and at-site frequency analysis, those of the estimated quantiles from circle and elliptical networks were small. In contrast, the sites where the quantile differences are big have big differences of quantiles from circle and elliptical networks. The estimated quantiles from the elliptical network are more accurate than those from the circle network, because the ellipse network contains more sites in South Korea. Moreover, the ellipse with ratio of 1 to 2.0 shows closer quantiles to those from index flood method than one with ratio of 1 to 1.5. It is, therefore, found that the FORGEX method with 1 to 2.0 ellipse network is appropriate regional frequency analysis in South Korea.

Selection of Variables for Regional Frequency Analysis of Annual Maximum Precipitation Using Multivariate Techniques

The regional frequency analysis is useful to estimate more accurate precipitation quantiles than the at-site frequency analysis, especially in case of regions with short record length like South Korea. In this study, the regionalization of annual maximum precipitation in South Korea was considered. The identification of homogeneous regions has a significant effect on quantile estimation in the regional frequency analysis. Various variables related to precipitation can be used to form regions. Since the type and number of variables may lead to improve the efficiency of partitioning, it is important to select those precipitation related variables, which represent most of the information from all candidate variables. Multivariate analysis techniques such as principal component analysis, factor analysis, and Procrustes analysis were used for this purpose. Finally, 33 variables were selected from the 42 candidate variables using multivariate techniques. A big loss of information due to dimension reduction was not found. Therefore, dimension reduction can promote the efficiency of cluster analysis. The selected variables can be successfully used to form regions for regional frequency analysis of annual maximum precipitation in South Korea.

A Study on the Predictive Power Improvement of Time Series Model with Empirical Mode Decomposition Method

The analysis of hydrologic time series data is crucial for the effective management of water resources. Therefore, it has been widely used for the long-term forecasting of hydrologic variables. In tradition, time series analysis has been used to predict a time series without considering exogenous variables. However, many studies using decomposition have been widely carried out with the assumption that one data series could be mixed with several frequent factors. In this study, the empirical mode decomposition method was performed for decomposing a hydrologic time series data into several components, and each component was applied to the time series models, autoregressive moving average (ARMA). After constructing the time series models, the forecasting values are added to compare the results with traditional time series model. Finally, the forecasted estimates from ARMA model with empirical mode decomposition method showed better performance than sole traditional ARMA model indicated from comparing the root mean square errors of the two methods.

Forecast of the Daily Inflow with Artificial Neural Network using Wavelet Transform at Chungju Dam

In this study, the daily inflow at the basin of Chungju dam is predicted using wavelet-artificial neural network for nonlinear model. Time series generally consists of a linear combination of trend, periodicity and stochastic component. However, when framing time series model through these data, trend and periodicity component have to be removed. Wavelet transform which is denoising technique is applied to remove nonlinear dynamic noise such as trend and periodicity included in hydrometeorological data and simple noise that arises in the measurement process. The wavelet-artificial neural network (WANN) using data applied wavelet transform as input variable and the artificial neural network (ANN) using only raw data are compared. As a results, coefficient of determination and the slope through linear regression show that WANN is higher than ANN by 0.031 and 0.0115 respectively. And RMSE and RRMSE of WANN are smaller than those of ANN by 37.388 and 0.099 respectively. Therefore, WANN model applied in this study shows more accurate results than ANN and application of denoising technique through wavelet transforms is expected that more accurate predictions than the use of raw data with noise.

ASYMPTOTIC VARIANCE OF REGIONAL GROWTH CURVE FOR GENERALIZED LOGISTIC DISTRIBUTION

The index flood method was introduced by Dalrymple to overcome the difficulties to obtain reliable estimates of the quantiles from relatively short record length. However, the application of index flood method must account for the additional uncertainty due to estimation of index flood at site. One must account for the uncertainties of the quantile estimates and those associated with the index flood. In this study, the generalized logistic distribution is considered as a an appropriate model for regional frequency analysis in Korea based on the method of probability weighted moments under the assumption that the regional quantiles and the index flood at site are independent. An approximate formulation of the variance of the quantile is introduced to evaluate the uncertainty of the estimated growth curve.

A Simulation Study for Effects of Various Factors Based on Generalized Logistic Distribution

The performance of the index flood method for 4 regions whose specifications are representatives of annual maximum rainfall data in South Korea, is investigated. The Monte Carlo simulation is performed to investigate the effects of at-site and regional frequency analysis, sample sizes, intersite dependence, heterogeneity, and applied frequency distributions on the accuracy of quantile estimates. Simulation experiments show that the regional frequency analysis is more accurate than the atsite frequency analysis even in the regions with moderate amounts of heterogeneity, intersite dependence, and misspecfication of the frequency distributions. Heterogeneity increases the relative bias of at-site estimates. Intersite dependence increases the variability of estimates but has little effect on the relative bias. As the nonexceedance probability increases, the performance of the regional frequency analysis over the at-site frequency analysis improves. It is also found that the misspecification of the frequency distribution is more important than heterogeneity as a source of error for large return periods.