• Atmosphere
• /
• v.25 no.2
• /
• pp.283-294
• /
• 2015

For urban flash flood simulation, we need the higher resolution radar rainfall than radar rainfall of KMA, which has 10 min time and 1km spatial resolution, because the area of subbasins is almost below $1km^2$. Moreover, we have to secure the high quantitative accuracy for considering the urban hydrological model that is sensitive to rainfall input. In this study, we developed the quantitative precipitation estimation (QPE), which has 250 m spatial resolution and high accuracy using KMA AWS and SK Planet stations with Mt. Gwangdeok radar data in Seoul area. As the results, the rainfall field using KMA AWS (QPE1) is showed high smoothing effect and the rainfall field using Mt. Gwangdeok radar is lower estimated than other rainfall fields. The rainfall field using KMA AWS and SK Planet (QPE2) and conditional merged rainfall field (QPE4) has high quantitative accuracy. In addition, they have small smoothed area and well displayed the spatial variation of rainfall distribution. In particular, the quantitative accuracy of QPE4 is slightly less than QPE2, but it has been simulated well the non-homogeneity of the spatial distribution of rainfall.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.58 no.3
• /
• pp.57-69
• /
• 2016

In this study, we analyzed the extreme rainfall distribution scenarios based on probable rainfall calculation and applying various time distribution models over the landslide high risk zones in urban areas. We used observed rainfall data form total 71 ASOS (Automated Synoptic Observing System) station and AWS (Automatic Weather Station) in KMA (Korea Meteorological Administration), and we analyzed the linear trends for 1-hr and 24-hr annual maximum rainfall series using simple linear regression method, which are identified their increasing trends with slopes of 0.035 and 0.660 during 1961-2014, respectively. The Gumbel distribution was applied to obtain the return period and probability precipitation for each duration. The IDF (Intensity-Duration-Frequency) curves for landslide high risk zones were derived by applying integrated probability precipitation intensity equation. Results from IDF analysis indicate that the probability precipitation varies from 31.4~38.3 % for 1 hr duration, and 33.0~47.9 % for 24 hr duration. It also showed different results for each area. The $Huff-4^{th}$ Quartile method as well as Mononobe distribution were selected as the rainfall distribution scenarios of landslide high risk zones. The results of this study can be used to provide boundary conditions for slope collapse analysis, to analyze sediment disaster risk, and to use as input data for risk prediction of debris flow.

• Water for future
• /
• v.14 no.4
• /
• pp.13-18
• /
• 1981

The rainfall pattern analysis on time distribution characteristics of rainfall rates in important in determination of design flow for hydraulic structures, particularly in urban area drainage network system design. The historical data from about 400 storm samples during 31 years in Seoul have been used to investigate the time distribution of 5-minute rainfall in the warm season. Time distribution relations have been deveolped for heavy stroms over 20mm in total rainfall and represented by relation percentage of total storm rainfall to percentage of total storm time and grouping the data according to the quartile in which rainfall was heaviest. And also time distribution presented in probability terms to provide quantitative information on inter-strom variability. The resulted time distribution relations are applicable to construction of rainfall hyetograph of design storm for determination of design flow hydrograph and identification of rainfall pattern at given watershed area. They can be used in conjuction with informations on spatstorm models for hydrologic applications. It was found that second-quartile storms occurred most frequently and fourth-quartile storms most infrequently. The time distribution characteristics resulted in this study have been presented in graphic forms such as time distribution curves with probability in cumulative percent of storm-time and precipitation, and selected histograms for first, second, third, and fourth quartile stroms.

• Journal of Environmental Science International
• /
• v.20 no.3
• /
• pp.329-340
• /
• 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 Environmental Science International
• /
• v.28 no.1
• /
• pp.19-35
• /
• 2019

This study investigates the performance of four Bayesian methods, Random Walk Metropolis (RWM), Hit-And-Run Metropolis (HARM), Adaptive Mixture Metropolis (AMM), and Population Monte Carlo (PMC), for estimating the parameters and uncertainties of probability rainfall distribution, and the results are compared with those of conventional parameter estimation methods; namely, the Method Of Moment (MOM), Maximum Likelihood Method (MLM), and Probability Weighted Method (PWM). As a result, Bayesian methods yield similar or slightly better results in parameter estimations compared with conventional methods. In particular, PMC can reduce parameter uncertainty greatly compared with RWM, HARM, and AMM methods although the Bayesian methods produce similar results in parameter estimations. Overall, the Bayesian methods produce better accuracy for scale parameters compared with the conventional methods and this characteristic improves the accuracy of probability rainfall. Therefore, Bayesian methods can be effective tools for estimating the parameters and uncertainties of probability rainfall distribution in hydrological practices, flood risk assessment, and decision-making support.

• Journal of Korea Water Resources Association
• /
• v.49 no.6
• /
• pp.481-493
• /
• 2016

The statistical analysis to the torrential rainfall data that is defined as a rainfall amount more than 80 mm/day is performed with Daegu and Busan rainfall data which is collected during 384 months. The number of occurrence of the torrential rainfall events can be simulated usually using Poisson distribution. However, the Poisson distribution can be frequently failed to simulate the statistical characteristics of the observed value when the observed data is zero-inflated. Therefore, in this study, Generalized Poisson distribution (GPD), Zero-Inflated Poisson distribution (ZIP), Zero-Inflated Generalized Poisson distribution (ZIGP), and Bayesian ZIGP model were used to resolve the zero-inflated problem in the torrential rainfall data. Especially, in Bayesian ZIGP model, a informative prior distribution was used to increase the accuracy of that model. Finally, it was suggested that POI and GPD model should be discouraged to fit the frequency of the torrential rainfall data. Also, Bayesian ZIGP model using informative prior provided the most accurate results. Additionally, it was recommended that ZIP model could be alternative choice on the practical aspect since the Bayesian approach of this study was considerably complex.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2007.05a
• /
• pp.1967-1971
• /
• 2007

In general, outflow is larger with rainfall but it is various in the initial moisture condition of basin and condition of rainfall distribution in both time and space. In this study, changes of outflow with time varied rainfall data were analyzed in the basin in which the moisture distribution is constant. Outflow differences with rainfall intensive of first period, middle period, and last period of month are 6.1% in January, 7.8% in February, 9.8% in March, 22.6% in April, 15.7% in May, 19.1% in June, 22.6% in July, 22.4% in August, and 16.8% in september respectably. The results show that 10 days outflow differences are ranged from 6.1% to 22.6% under the constant moisture condition, Outflow differences in the flood seasons are larger than them in the drought seasons.

• Journal of Korea Water Resources Association
• /
• v.32 no.3
• /
• pp.247-254
• /
• 1999

The frequency analyses of annual maximum rainfall data for 22 rainfall gauging stations is Korea were performed. The method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) were used in parameter estimation. The GEV distribution was selected as an appropriate model for annual maximum rainfall data based on parameter validity condition, graphical analysis, separation effect, and goodness of fit tests. For the selected GEV model, spatial analysis was performed and rainfall intensity-duration-frequency equation was derived by using linearization technique. The derived rainfall intensity-duration-frequency equation can be used for estimating rainfall quantiles of the selected stations with convenience and reliability in practice.

• Journal of Wetlands Research
• /
• v.12 no.1
• /
• pp.51-61
• /
• 2010

Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Therefore, it is important to understand the spatial-temporal features of rainfall. In this study, RADAR rainfall was used to calculate gridded areal rainfall which reflects the spatial-temporal variability. In addition, Kalman-filter method, a stochastical technique, was used to combine ground rainfall network with RADAR rainfall network to calculate areal rainfall. Thiessen polygon method, Inverse distance weighting method, and Kriging method were used for calculating areal rainfall, and the calculated data was compared with adjusted areal RADAR rainfall measured using the Kalman-filter method. The result showed that RADAR rainfall adjusted with Kalman-filter method well-reproduced the distribution of raw RADAR rainfall which has a similar spatial distribution as the actual rainfall distribution. The adjusted RADAR rainfall also showed a similar rainfall volume as the volume shown in rain gauge data. Anseong-Cheon basin was used as a study area and the RADAR rainfall adjusted with Kalman-filter method was applied in $Vflo^{TM}$ model, a physical-based distributed model, and ModClark model, a semi-distributed model. As a result, $Vflo^{TM}$ model simulated peak time and peak value similar to that of observed hydrograph. ModClark model showed good results for total runoff volume. However, for verifying the parameter, $Vflo^{TM}$ model showed better reproduction of observed hydrograph than ModClark model. These results confirmed that flood runoff simulation is applicable in domestic settings(in South Korea) if highly accurate areal rainfall is calculated by combining gauge rainfall and RADAR rainfall data and the simulation is performed in link to the distributed hydrological model.

• Journal of the Korean Statistical Society
• /
• v.34 no.4
• /
• pp.311-329
• /
• 2005

Skewed t distributions have attracted significant attention in the last few years. In this paper, a generalization - referred to as the skewed generalized t distribution - with the pdf f(x) = 2g(x)G(${\lambda}x$) is introduced, where g(${\cdot}$) and G (${\cdot}$) are taken, respectively, to be the pdf and the cdf of the generalized t distribution due to McDonald and Newey (1984, 1988). Several particular cases of this distribution are identified and various representations for its moments derived. An application is provided to rainfall data from Orlando, Florida.