• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.5
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• pp.343-351
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• 2012

In this study, shallow-water design waves are calculated for the return period of 10, 20, 30, and 50 years, based on the extreme value analysis of the wave measurement data at Gangneung beach. These values are compared with the results of SWAN simulation with the boundary condition of the deep-water design waves of the corresponding return periods at the Gangneung sea area provided by the Fisheries Agency (FA, 1988) and Korea Ocean Research & Development Institute (KORDI, 2005). It is found that the shallow-water wave heights at Gangneung beach calculated by the deep-water design waves were significantly less than the observation data. As the return period becomes higher, the significant wave heights obtained by the extreme value analysis becomes higher than those computed by SWAN with the deep-water design waves of the corresponding return periods. KORDI computed the hindcast wave data from January 2004 to August 2008 by WAM with a finer-grid mesh system than those of previous studies. Comparisons of the wave hindcast results with the wave observation show that the reproducibility of the winter-season storm wave was considerably improved compared to the hindcast data from 1979 to 2003. Hereafter, it is necessary to carry out hindcast wave data for the years before 2004 using WAM with the finer-grid mesh system and to supplement the deep-water design wave.

• Journal of Wetlands Research
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• v.12 no.2
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• pp.13-23
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• 2010

According to recent researches on climate change, the global warming is obvious to increase rainfall intensity. Damage caused by extreme hydrologic events due to global change is steadily getting bigger and bigger. Recently, frequently occurring heavy rainfalls surely affect the trend of rainfall observations. Probability precipitation estimation method used in designing and planning hydrological resources assumes that rainfall data is stationary. The stationary probability precipitation estimation method could be very weak to abnormal rainfalls occurred by climate change, because stationary probability precipitation estimation method cannot reflect increasing trend of rainfall intensity. This study analyzed temporal variation of trend in rainfall time series at 51 stations which are not significant for statistical trend tests. After modeling rainfall time series with maintaining observed statistical characteristics, this study also estimated whether rainfall data is significant for the statistical trend test in near future. It was found that 13 stations among sample stations will have trend within 10 years. The results indicate that non-stationary probability precipitation estimation method must be applied to sufficiently consider increase trend of rainfall.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.151-163
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• 2023

This study explored the 2022 drought over the Nakdong River watershed. Here, we developed a bivariate regional frequency analysis method to evaluate the risk of hydrological drought. Currently, natural streamflow data are generally limited to accurately estimating the drought frequency. Under this circumstance, the existing at site frequency analysis can be problematic in estimating the drought risk. On the other hand, a regional frequency analysis could provide a more reliable estimation of the joint return periods of drought variables by pooling available streamflow data over the entire watershed. More specifically, the Copula-based regional frequency analysis model was proposed to effectively take into account the tail dependencies between drought variables. The results confirmed that the regional frequency analysis model showed better performance in model fit by comparing the goodness-of-fit measures with the at-site frequency analysis model. We find that the estimated joint return period of the 2022 drought in the Nakdong River basin is about eight years. In the case of the Nam river Dam, the joint return period was approximately 20 years, which can be regarded as a relatively severe drought over the last three decades.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of Korea Water Resources Association
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• v.53 no.1
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• pp.45-56
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• 2020

This study aims to quantify the uncertainty that can be induced by the objective function when calibrating SWAT parameters using SWAT-CUP. SWAT model was constructed to estimate runoff in Naesenong-cheon, which is the one of mid-watershed in Nakdong River basin, and then automatic calibration was performed using eight objective functions (R², bR², NS, MNS, KGE, PBIAS, RSR, and SSQR). The optimum parameter sets obtained from each objective function showed different ranges, and thus the corresponding hydrologic characteristics of simulated data were also derived differently. This is because each objective function is sensitive to specific hydrologic signatures and evaluates model performance in an unique way. In other words, one objective function might be sensitive to the residual of the extreme value, so that well produce the peak value, whereas ignores the average or low flow residuals. Therefore, the hydrological similarity between the simulated and measured values was evaluated in order to select the optimum objective function. The hydrologic signatures, which include not only the magnitude, but also the ratio of the inclining and declining time in hydrograph, were defined to consider the timing of the flow occurrence, the response of watershed, and the increasing and decreasing trend. The results of evaluation were quantified by scoring method, and hence the optimal objective functions for SWAT parameter calibration were determined as MNS (342.48) and SSQR (346.45) with the highest total scores.

• Water for future
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• v.10 no.1
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• pp.39-51
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• 1977

The purpose of this study is to make "The Rainfall Frequency Atlas in Korea" by the analytical method with new hydrological concepts. In this study, all of the rainfall datas in Korea was used for surveying of the basic data, and so we can get 103 sites for annual rainfall data and 100 sites for the max. in a day that are suitable to the purpose of the study. The above data groups are possible to estimate the normal standard period by the moving average method with $\pm$5% of significance level of variance ratio between the max. and min. moving average and arithmetic mean, but it may be impossible to study until 1990's for the short duration under 18-hr because the sites, having the short duration data, are only 12. The results of this study are as follows; 1. The normal standard period estimated by the moving average method is 20 year with $\pm$5% of significance level of variance ratio, and 30 year with$\pm$2-3%. 2. For the annual and max. rainfall in a day, it is possible to make the rainfall frequency atlas with 30normal standard period, but it may be impossible until 1990's for short duration. 3. "Y-k method" developed by writer is best suitable in the rainfal frequency analysis in Korea because of its convenience and reduction in the amount of calculation compared with other methods. 4. To improve the utilization of the rainfall frequency atlas, the larger-sized and the more detailed iso-precipitation atlas must be drawn.atlas must be drawn.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.2
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• pp.83-94
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• 2010

The objective of this study was to determine the best probability distributions of annual, seasonal and monthly precipitation in Korea. Data observed at 32 stations in Korea were analyzed using the L-moment ratio diagram and the average weighted distance (AWD) to identify the best probability distributions of each precipitation. The probability distribution was best represented by 3-parameter Weibull distribution (W3) for the annual precipitation, 3-parameter lognormal distribution (LN3) for spring and autumn seasons, and generalized extreme value distribution (GEV) for summer and winter seasons. The best probability distribution models for monthly precipitation were LN3 for January, W3 for February and July, 2-parameter Weibull distribution (W2) for March, generalized Pareto distribution (GPA) for April, September, October and November, GEV for May and June, and log-Pearson type III (LP3) for August and December. However, from the goodness-of-fit test for the best probability distributions of the best fit, GPA for April, September, October and November, and LN3 for January showed considerably high reject rates due to computational errors in estimation of the probability distribution parameters and relatively higher AWD values. Meanwhile, analyses using data from 55 stations including additional 23 stations indicated insignificant differences to those using original data. Further studies using more long-term data are needed to identify more optimal probability distributions for each precipitation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Journal of Wetlands Research
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• v.16 no.1
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• pp.19-32
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• 2014

The purpose of this paper is to obtain reliable rainfall data for runoff simulation and other hydrological analysis by the calibration of gauge rainfall. The calibrated gauge rainfall could be close to the actual value with rainfall on the ground. In order to analyze the wind effect of ground rain gauge, we selected the rain gauge sites with and without a windshield and standard rain gauge data from Chupungryeong weather station installed by standard of WMO. Simple linear regression model and artificial neural networks were used for the calibration of rainfalls, and we verified the reliability of the calibrated rainfalls through the runoff analysis using $Vflo^{TM}$. Rainfall calibrated by linear regression is higher amount of rainfall in 5%~18% than actual rainfall, and the wind remarkably affects the rainfall amount in the range of wind speed of 1.6~3.3m/s. It is hard to apply the linear regression model over 5.5m/s wind speed, because there is an insufficient wind speed data over 5.5m/s and there are also some outliers. On the other hand, rainfall calibrated by neural networks is estimated lower rainfall amount in 10~20% than actual rainfall. The results of the statistical evaluations are that neural networks model is more suitable for relatively big standard deviation and average rainfall. However, the linear regression model shows more suitable for extreme values. For getting more reliable rainfall data, we may need to select the suitable model for rainfall calibration. We expect the reliable hydrologic analysis could be performed by applying the calibration method suggested in this research.

• Journal of Digital Convergence
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• v.9 no.3
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• pp.69-79
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• 2011

This study is the text discourse of agenda setting through media policy on the three communities. The materials of subjects are the 71 text discourses that appeared in the columns, the special manuscripts, and the comments on the contemporary topics in 33 media. The subjects focuses on the metaphor, metonymy, and binary transposition. This kind of connotation tends to be imploded into people through media, so that it produces hyperreality. This process produces the regulation and strengthens the reality through the circuit of culture. Thus this research tries to develop the theoretical foundation for analysing the text discourse produced by the media. Also it focuses on widening the research scope to study the effects that the circuit of culture provides on the politics, society, and economics. Therefore The first, the objective meanings(denotation)which the referents of the community as T'PALACE, I'PARK, and STARCITY are 'larger scale', 'high and skyscraper', 'the rich people and the plutocrats who have very high academic career' and ' the residence place for the famous stars and successful CEOs', etc. and the subjective meanings, connotations which the referents of the community are 'The first street' transposes '1%' 'their own space' into the characteristics of the wealth of Gangnam district or Korean wealth', the additional significations which the metaphors such as 'the noble community', 'the sample for the high -level residential space', and 'the greed of 1%'. Conclusion, The significations of the symbols became imploded into the population and circulated along with the cultural streams through the media. The referents are recreated and consumed among the other communities such as the named 'PALACE', 'I'PARK', 'STARCITY' in the other areas. This kind of ideology tends to create the myths such as 'the 1% rich people of Gangnam', 'the first street of Korean wealth', and create the regulation such as 'the compound taxes for the real-estates', 'the policy of reducing the taxes for the rich', 'the policy of reducing the taxes for the 1% of the rich'. Also these regulations make the politicians operate new policies and are being utilized as 'slogan' for the politicians.