• Journal of the Korean earth science society
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• v.36 no.5
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• pp.460-475
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• 2015

A number of heavy rainfall events on the Korean Peninsula are indirectly influenced by tropical cyclones (TCs) when they are located in southeastern China. In this study, a heavy rainfall case in the middle Korean region is selected to examine the influence of typhoon simulation performance on predictability of remote rainfall over Korea as well as direct rainfall over Taiwan. Four different numerical experiments are conducted using Weather Research and Forecasting (WRF) model, toggling on and off two different improvements on typhoon in the model initial condition (IC), which are TC bogussing initialization and dropwindsonde observation data assimilation (DA). The Geophysical Fluid Dynamics Laboratory TC initialization algorithm is implemented to generate the bogused vortex instead of the initial typhoon, while the airborne observation obtained from dropwindsonde is applied by WRF Three-dimensional variational data assimilation. Results show that use of both TC initialization and DA improves predictability of TC track as well as rainfall over Korea and Taiwan. Without any of IC improvement usage, the intensity of TC is underestimated during the simulation. Using TC initialization alone improves simulation of direct rainfall but not of indirect rainfall, while using DA alone has a negative impact on the TC track forecast. This study confirms that the well-suited TC simulation over southeastern China improves remote rainfall predictability over Korea as well as TC direct rainfall over Taiwan.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.164-164
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• 2016

Radar rainfall estimates have been widely used in calculating rainfall amount approximately and predicting flood risks. The radar rainfall estimates have a number of error sources such as beam blockage and ground clutter hinder their applications to hydrological flood forecasting. Moreover, it has been reported in paper that those errors are inter-correlated spatially and temporally. Therefore, in the current study, we tested influence about spatio-temporal errors in radar rainfall estimates. Spatio-temporal errors were simulated through a stochastic simulation model, called Multivariate Autoregressive (MAR). For runoff simulation, the Nam River basin in South Korea was used with the distributed rainfall-runoff model, Vflo. The results indicated that spatio-temporal dependent errors caused much higher variations in peak discharge than spatial dependent errors. To further investigate the effect of the magnitude of time correlation among radar errors, different magnitudes of temporal correlations were employed during the rainfall-runoff simulation. The results indicated that strong correlation caused a higher variation in peak discharge. This concluded that the effects on reducing temporal and spatial correlation must be taken in addition to correcting the biases in radar rainfall estimates. Acknowledgements This research was supported by a grant from a Strategic Research Project (Development of Flood Warning and Snowfall Estimation Platform Using Hydrological Radars), which was funded by the Korea Institute of Construction Technology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3287-3295
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• 2009

This present study implemented a rainfall simulation system, and performed simulation and numerical analysis according to rainfall and slope conditions using a model slope built with weathered granite soil. Extensive analysis were conducted on the characteristics of changes in volumetric water content and pore water pressure measured in the simulation, and compared them with the results of numerical analysis. It took longer for the volumetric water content to reach the limit when rainfall intensity was high and the slope was steep and shorter when rainfall intensity was low and the slope was gentle. When rainfall intensity was low and the duration of rainfall was short, negative pore water pressure was higher and the time for restoration was shorter. On the contrary, when rainfall intensity was high and the duration of rainfall was long, it took a longer time to restore negative pore water pressure. In the results of rainfall simulation and numerical analysis, the distribution of volumetric water content and pore water pressure was similar between the two. However, the volumetric water content was different by up to 5%, and pore water pressure by up to 3kPa.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.3_4
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• pp.48-60
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• 1995

A flood - flow forecasting system model of river basins has been developed in this study. The system model consists of the data management system(the observation and telemetering system, the rainfall forecasting and data-bank system), the flood runoff simulation system, the reservoir operation simulation system, the flood forecasting simulation system, the flood warning system and the user's menu system. The Multivariate Rainfall Forecasting model, Meteorological factor regression model and Zone expected rainfall model for rainfall forecasting and the Streamflow synthesis and reservoir regulation(SSARR) model for flood runoff simulation have been adopted for the development of a new system model for flood - flow forecasting. These models are calibrated to determine the optimal parameters on the basis of observed rainfall, 7 streamfiow and other hydrological data during the past flood periods.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.118-129
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• 2021

The sediment transportation caused by soil erosion due to rainfall-discharge in the large watershed scale plays critical role in human society. The relationship between rainfall-discharge-sediment transportation is depending on the start time of rainfall and end of rainfall but, the studies related with rainfall characteristics are insufficient. In this study, The Soil and Water Assession Tool (SWAT) model was used to study the relationship between rainfall-discharge-sediment transportation at the Sook river watershed which is monitored by the Ministry of Environment. To do this, first of all, the sensitivity analysis about model attributes was performed using monitored data. The accuracy analysis of SWAT model was conducted using the model's efficiency index (Nash and Sutcliffe model efficiency; NSE) and the coefficient of determination (R²). After that, it was studied what results could be obtained according to changes in rainfall timing and end points. In the result of discharge simulation, the modified rainfall values (sum of total rainfall starting time and end time) showed more high accuracy values (R²:0.90, NSE: 0.8) than original rainfall values (R²:0.76, NSE: 0.72). In the result of sediment transportation simulation, during calibration had more resonable results(R²:0.87, NSE: 0.86) than compared with original rainfall values (R²:0.44, NSE: 0.41). However, validation results of sediment transportation simulation showed low accuracy values compared with calibration results. This results maybe cause monitoring periods of sediment flow compared with discharge monitoring periods. Nevertheless, since rainfall characteristic plays critical rule in model results, continuous research on rainfall characteristic is needed.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.141-148
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• 2019

When simulating the daily rainfall amount by existing Markov Chain model, it is general to simulate the rainfall occurrence and to estimate the rainfall amount randomly from the distribution which is similar to the daily rainfall distribution characteristic using Monte Carlo simulation. At this time, there is a limitation that the characteristics of rainfall intensity and distribution by time according to the rainfall duration are not reflected in the results. In this study, 1-day, 2-day, 3-day, 4-day rainfall event are classified, and the rainfall amount is estimated by rainfall duration. In other words, the distributions of the total amount of rainfall event by the duration are set using the Kernel Density Estimation (KDE), the daily rainfall in each day are estimated from the distribution of each duration. Total rainfall amount determined for each event are divided into each daily rainfall considering the type of daily distribution of the rainfall event which has most similar rainfall amount of the observed rainfall using the k-Nearest Neighbor algorithm (KNN). This study is to develop the limitation of the existing rainfall estimation method, and it is expected that this results can use for the future rainfall estimation and as the primary data in water resource design.

• Journal of Wetlands Research
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• v.5 no.2
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• pp.1-13
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• 2003

In Korea, most of the rainfalls have been concentrated in the flood season and the flood study has received more attention than low flow analysis. One of the reasons that the analysis of low flows has less attention is the lacks of the required data like daily rainfall and so we have used the stochastic processes such as pulse noise, exponential distribution, and state model of Markov chain for the rainfall simulation in short term such as daily. Especially this study will pay attention to the state model of Markov chain. The previous study had performed the simulation study by the state model without considerations of the flood and non-flood periods and without consideration of the frequency of rainfall for the period of a state. Therefore this study considers afore mentioned two cases and compares the results with the known state model. As the results, the RMSEs of the suggested and known models represent the similar results. However, the PRE(relative percentage error) shows the suggested model is better results.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.605-612
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• 2006

In this study(II), The developed rainfall forecast model was applied to the NakDong River Basin for the heavy rainfall on 6th to 16th of August in 2002. The results demonstrated that the rainfall forecasts of 3 hours lead time showed good agreement with observed data. The inundation aspect of simulation depends on actual levee failure in the same basin. Rainfall forecasts were used for flood amount computation in the target watershed. Also the flood amount in the target watershed was used on boundary condition for flood inundation simulation in a protected lowland and a river. The results of simulation are consistent with actuality inundation traces and flood level data of the target watershed. This study provides practical applicability of satellite data in rainfall forecast of extreme events such as heavy rainfall or typhoon. Also this study presented an advanced integrated model of rainfall, runoff, and inundation analysis which can be applicable for flood disaster prevention and mitigation.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.180-180
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• 2015

Weather radar has been widely used in measuring precipitation and discharge and predicting flood risks. The radar rainfall estimate has one of the essential problems in terms of uncertainty and accuracy. Previous study analyzed radar errors to reduce its uncertainty or to improve its accuracy. Furthermore, a recent analyzed the effect of radar error on rainfall-runoff using spatial error model (SEM). SEM appropriately reproduced radar error including spatial correlation. Since the SEM does not take the time dependence into account, its time variability was not properly investigated. Therefore, in the current study, we extend the SEM including time dependence as well as spatial dependence, named after Spatial-Temporal Error Model (STEM). Radar rainfall events generated with STEM were tested so that the peak runoff from the response of a basin could be investigated according to dependent error. The Nam River basin, South Korea, was employed to illustrate the effects of STEM on runoff peak flow.

• Water for future
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• v.26 no.4
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• pp.117-125
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• 1993

A flood -flow management system model of river basin has been developed in this study. The system model consists of the observation and telemetering system, the rainfall forecasting and data-bank system, the flood runoff simulation system, the dam operation simulation system, the flood forecasting simulation system and the flood warning system. The Multivariate model(MV) and Meterological-factor regression model(FR) for rainfall forecasting and the Streamflow synthesis and reservoir regulation(SSARR) model for flood runoff simulation have been adopted for the development of a new system model for flood-flow management. These models are calibrated to determine the optimal parameters on the basis of observed rainfall, streamflow and other hydrological data during the past flood periods. The flood-flow management system model with SSARR model(FFMM-SR,FFMM-SR(FR) and FFMM-SR(MV)), in which the integrated operation of dams and rainfall forecasting in the basin are considered, is then suggested and applied for flood-flow management and forecasting. The results of the simulations done at the base stations are analysed and were found to be more accurate and effective in the FFMM-SR and FFMM0-SR(MV).