• Korean Journal of Soil Science and Fertilizer
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• v.16 no.2
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• pp.112-118
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• 1983

Rainfall factor (R-factor), which is an index for the prediction of soil erosion in the Universal Soil Loss Equation (USLE), was computed from 21 years rainfall data at 51 locations in Korea. The values of R-factor are from 200 to 300 in the eastern part, and 300 to 700 in the western and southern part of the peninsula. Curvilinear regressions exist between annual rainfall and annual R-factor or between monthly rainfall and monthly R-factor. The R-factor can be estimated from the regression equation as a function of the amount of rainfall. According to the comparison between the actual soil loss measured by lysimeter and the soil loss predicted by the USLE, EI 30 for R-factor was recognized as a suitable factor for the USLE in korea.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.995-1008
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• 2005

In this study, the principal components of rainfall in Korea are extracted by a method which consists of the independent component analysis combined with the wavelet transform, to examine the spatial correlation between seasonal rainfalls and global sea surface temperatures (SSTs). The 2-8 year band retains a strong wavelet power spectrum and the low frequency characteristics are shown by the wavelet analysis. The independent component analysis is performed by using the Scale Average Wavelet Power(SAWP) that is estimated by wavelet analysis. Interannual-interdecadal variation is the dominant variation, and an increasing trend is observed in the spring and summer seasons. The relationships between principal components of rainfall in the spring/summer seasons and SSTs existed in Indian and Pacific Oceans. Particularly, the SST zones, which represent a statistically significant correlation are located in the Philippine offshore and Australia offshore. Also, the three month leading SSTs in the same region we strongly correlated with the rainfall. Hence, these results propose a promising possibility of seasonal rainfall prediction by SST predictors.

• Water for future
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• v.27 no.2
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• pp.147-154
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• 1994

The occurrence condition of debris fiow due to rainfall is given by solving the equations for fiow on a slope. The solution shows that a debris fiow will occur on a slope when the accumulated rainfall within the time of concentration exceeds a certain value determined by the properties of the slope. To estimate this critical value, the system analysis technique would be commendable. In this study, a procedure to fine the critical rainfall from the rainfall data whith and without debris flows is proposed. Reliability of this method is verified by applying to the debris flows in Unzen Volcano which recently began to erupt. Discharge of debris flow in a stream is obtained by solving the equation of continuity using the kinematic wave theory and assuming the cross sectional area to be a function of discharge. The computed hydrographs agree weel with the ones observed at the rivers in Sakurajima and Unzen Volcanos. It is found from the derived equation that the runoff intensity of debris flow is in proportion to the rainfall intensity and accumulated rainfall, jointly. This gives a theoretical basis to the conventional method which has been widely used.

• Journal of Korea Water Resources Association
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• v.43 no.4
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• pp.337-351
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• 2010

There is a growing dissatisfaction with use of conventional statistical methods for the prediction of extreme events. Conventional methodology for modeling extreme event consists of adopting an asymptotic model to describe stochastic variation. However asymptotically motivated models remain the centerpiece of our modeling strategy, since without such an asymptotic basis, models have no rational for extrapolation beyond the level of observed data. Also, this asymptotic models ignored or overestimate the uncertainty and finally decrease the reliability of uncertainty. Therefore this article provide the research example of the extreme rainfall event and the methodology to reduce the uncertainty. In this study, the Bayesian MCMC (Bayesian Markov Chain Monte Carlo) and the MLE (Maximum Likelihood Estimation) methods using a quadratic approximation are applied to perform the at-site rainfall frequency analysis. Especially, the GEV distribution and Gumbel distribution which frequently used distribution in the fields of rainfall frequency distribution are used and compared. Also, the results of two distribution are analyzed and compared in the aspect of uncertainty.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.285-288
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• 2008

This study aims to rout optimized design flood discharge through prediction of the frequency-based precipitation from the frequency analysis with density of rainfall gage networks in urban watershed. Frequency analysis was examined for the measured rainfall depth with low density of a point and high density of the sub-basin divided into 13 points in watershed. The used rainfall data in order to analyze consists of two groups based on measured rainfall depth for a day duration with 39years of a point and 6years of 13 points by an extending as annual exceedance series, respectively. Selected rainfall data in this analysis show that low-network has maximum rainfall depth with duration 1hr-79.1mm and 24hrs-329.1mm, and high-networks have ones with duration of 1hr-93.0 mm and 24 hrs-245.0 mm, respectively. As the result, probability of the best in this study determined the Gumbel method from the goodness of fit test and the method of prime 6 probability distributions.

• Journal of Korea Water Resources Association
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• v.46 no.5
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• pp.439-447
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• 2013

The applicability of the parameter map of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) model for the Korean Peninsula was assessed from the perspective of flood prediction. The design rainfalls estimated from the MBLRP model were smaller than those from observed values by 5% to 40%, and the degree of underestimation of design rainfall increases with the increase of the recurrence interval of the design rainfall. The design floods at a virtual watershed estimated using the simulated rainfall time series based on MBLRP model were also smaller than those derived from the observed rainfall time series by 20% to 45%. The degree of underestimation of design flood increases with the increase of the recurrence interval of the design flood.

• Fire Science and Engineering
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• v.24 no.2
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• pp.67-75
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• 2010

The fuel moisture changes accompanying with the elapsed days after a rainfall is very important to predict the risk of forest fire and make a good use of forest fire guard. So, to investigate the conditions for the risk of forest fire, it was studied the risk of forest fire for fallen leaves level, rotten level, and soil level after more-than-5 mm-rainfall according to the different forest density of pine forests which were located in Yeong-dong region in the Spring of 2007. The result of the study showed that the around 17% of fuel moisture which was the risky level for forest fire was reached after three days of a rainfall in the coarse dense forest region and after five days in the medium or highly dense forest region. However, for the rotten level represents more than 30% of fuel moisture even after six days after the rainfall, and the lower and upper level of the soil represented a slight or almost no changes. Based on the result, the prediction model ($R^2$=0.56~0.87) for the change of fuel moisture was developed, and it was examined by applying to actual meteorological measurements in the same period of 2008. It showed a meaningful result of 1% level of distinction.

• Journal of Digital Contents Society
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• v.17 no.1
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• pp.51-57
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• 2016

Distributed Computing attempts to harness a massive computing power using a great numbers of idle PCs resource distributed linked to the internet and processes a variety of applications parallel way such as bio, climate, cryptology, and astronomy. In this paper, we develop internet-distributed computing environment, so that we can analyze High Resolution Rainfall Prediction application in meteorological field. For analyze the rainfall forecast in Korea peninsula, we used QPM(Quantitative Precipitation Model) that is a mesoscale forecasting model. It needs to a lot of time to construct model which consisted of 27KM grid spacing, also the efficiency is degraded. On the other hand, based on this model it is easy to understand the distribution of rainfall calculated in accordance with the detailed topography of the area represented by a small terrain model reflecting the effects 3km radius of detail and terrain can improve the computational efficiency. The model is broken down into detailed area greater the required parallelism and increases the number of compute nodes that efficiency is increased linearly.. This model is distributed divided in two sub-grid distributed units of work to be done in the domain of $20{\times}20$ is networked computing resources.

• Journal of the Korean Geosynthetics Society
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• v.10 no.4
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• pp.71-79
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• 2011

In this study, the variation of wetting front and ground water level at the embankment constructed in the Saemangeum area were predicted considering rainfall duration times and the slope stability analysis of the embankment was carried out according to prediction results of wetting front and ground water level. The embankment was formed by dredging soils. A suction stress, a cohesion and a frictional angle of dreding soils measured by soil tests were applied to estimate the unsaturated soil properties. According to the analysis results of the wetting front and the ground water level for various rainfall duration time, the wetting front began to descend from the upper part of embankment at the beginning time of rainfall and after 1 hour of rainfall duration time. After that, the ground water level continued to ascend as the rainfall duration time was getting longer. After rainfall, the ground water level was distributed at a certain depth, and the ground water level was gradually descending as time goes by. According to the slope stability analysis of the embankment considering the variation of the wetting front and the ground water level, the safety factor of slope was rapidly reduced as the rainfall began to infiltrate into the ground, and the minimum safety factor of slope was estimated after 24 hours of rainfall duration time. Meanwhile, the safety factor of slope was increased with regaining the matric suction in the ground after rainfall.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.2
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• pp.179-185
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• 2016

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.