• Journal of Korea Water Resources Association
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• v.42 no.9
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• pp.703-713
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• 2009

Dam has very important roles in both water use and flood control. Dam release and runoff from rainfall affect directly to the flood control at the downstream of dam during heavy storm especially. This study evaluates the applicability of a distributed model by applying the GRM (Grid based Rainfall-runoff Model) based on HyGIS (Hydro Geographic Information System) environment to runoff modeling at the downstream of dam where the discharge from dam and rainfall affect simultaneously. In order to do this, Yeoju watershed in Han River basin is selected. Rainfall data and discharge from Chungju regulation dam and Hoengseong dam are applied to runoff simulation. The modeling results are verified with Yeoju water level station, and they show good agreement with observed hydrographs. And this study shows that GRM is able to simulate appropriately the effect of dam discharge and rainfall on watershed runoff.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.655-669
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• 2018

In this study, we developed a hybrid forecasting model based on a four-parameter distribution which allows a simultaneous season-ahead forecasting for both seasonal rainfall and sub-daily rainfall in Han-River and Geum-River basins. The proposed model is mainly utilized a set of time-varying predictors and the associated model parameters were estimated within a Bayesian nonstationary rainfall frequency framework. The hybrid forecasting model was validated through an cross-validatory experiment using the recent rainfall events during 2014~2017 in both basins. The seasonal precipitation results showed a good agreement with the observations, which is about 86.3% and 98.9% in Han-River basin and Geum-River basin, respectively. Similarly, for the extreme rainfalls at sub-daily scale, the results showed a good correspondence between the observed and simulated rainfalls with a range of 65.9~99.7%. Therefore, it can be concluded that the proposed model could be used to better consider climate variability at multiple time scales.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.2215-2219
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• 2009

For model calibration in rainfall-runoff modeling, streamflow data at a specific outlet is obviously required but is not sufficient to identify parameters of a model since numerous parameter combinations can result in very similar model performance measures (i.e. objective functions) and indistinguishable simulated hydrographs. This phenomenon has been called 'equifinality' due to inherent parameter uncertainty involved in rainfall-runoff modeling. This study aims to investigate catchment responses in time and space to various uncertain parameter sets in distributed rainfall-runoff modeling. Seven plausible (or behavioral) parameter sets, which guarantee identically-good model performances, were sampled using deterministic and stochastic optimization methods entitled SCE and SCEM, respectively. Then, we applied them to a computational tracer method linked with a distributed rainfall-runoff model in order to trace and visualize potential origins of streamflow at a catchment outlet. The results showed that all hydrograph simulations based on the plausible parameter sets were performed equally well while internal catchment responses to them showed totally different aspects; different parameter values led to different distributions with respect to the streamflow origins in space and time despite identical simulated hydrographs. Additional information provided by the computational tracer method may be utilized as a complementary constraint for filtering out non-physical parameter set(s) (or reducing parameter uncertainty) in distributed rainfall-runoff modeling.

• Environmental Engineering Research
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• v.18 no.1
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• pp.9-20
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• 2013

In this paper, the rainfall elasticity of streamflow was estimated to quantify the effects of climate change on 5 river basins. Rainfall elasticity denotes the sensitivity of annual streamflow for the variations of potential annual rainfall. This is a simple, useful method that evaluates how the balance of a water cycle on river basins changes due to long-term climate change and offers information to manage water resources and environment systems. The elasticity method was first used by Schaake in 1990 and is commonly used in the United States and Australia. A semi-distributed hydrological model (SLURP, semi-distributed land use-based runoff processes) was used to simulate the variations of area streamflow, and potential evapotranspiration. A nonparametric method was then used to estimate the rainfall elasticity on five river basins of Korea. In addition, the A2 (SRES IPCC AR4, Special Report on Emission Scenarios IPCC Fourth Assessment Report) climate change scenario and stochastic downscaling technique were used to create a high-resolution weather change scenario in river basins, and the effects of climate change on the rainfall elasticity of each basin were then analyzed.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.465-474
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• 2010

Real-time rainfall monitoring is of great practical importance over the highly populated Indochina area, which is prone to natural disasters, in particular in association with rainfall. With the goal of d etermining near real-time half-hourlyrain estimates from satellite, the three-layer, artificial neural networks (ANN) approach was used to train the brightness temperatures at 6.7, 11, and $12-{\mu}m$ channels of the Japanese geostationary satellite MTSAT against passive microwavebased rain rates from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and TRMM Precipitation Radar (PR) data for the June-September 2005 period. The developed model was applied to the MTSAT data for the June-September 2006 period. The results demonstrate that the developed algorithm is comparable to the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) results and can be used for flood monitoring across the Indochina area on a half-hourly time scale.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2009.04a
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• pp.269-270
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• 2009

This study builds a sediment rating curve using the measured sediment yield and the simulated soil erosion by a GIS-embedded empirical model. Then the structured sediment rating curve is used to determine the SDR on a basin scale in southern Korea. The whole data(year of 2002-2008) are divided into two groups and the first group(year of 2002-2005) is used for calibration, while the other is used for validation. Two cases(rainfall amount and rainfall intensity) are analyzed to consider the rainfall runoff erosivity factor in simulating soil erosion. The results show the derived SDR provides reasonable accuracy and rainfall intensity gives better performance in calculating soil erosion than rainfall amount.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.1020-1025
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• 2009

Recently, unusual change of weather occurred in world wide region causes localized heavy rainfall and consequently disasters like landslide and debris flow in steep slope area. And the main factors of these disasters are rainfall and forest fire. To verify the existing landslide prediction and warning system, information about landslide and rainfall were collected for a data base system and analysed.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.478-489
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• 2014

Recently, flood damage by frequent localized downpours in cities are on the increase on account of abnormal climate phenomena and growth of impermeable area by urbanization. In this study, we are focused on flooding on roads which is the basis of all means of transportation. To calculate real-time accumulated rainfall on a road link, we use the Coefficient of Correlation Weighting method (CCW) which is one of the revised methods of missing rainfall as we consider a road link as a unobserved rainfall site. CCW and real-time accumulated rainfall entered through the Internet are used to estimate the real-time rainfall on a road link. Together with the real-time accumulated rainfall, flooding history, rainfall range causing flooding of a road link and frequency probability precipitation for road design are used as factors to determine the Flood Risk Index on roads. We simulated two cases in the past, July, 7th, 2009 and July, 15th, 2012 in Busan. As a result, all of road links included in the actual flooded roads at that time got the high level of flood risk index.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.736-744
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• 2009

The abrupt failure of slope caused by a concentrated rainfall would be a disaster in this country. Specially, the soil slope may be collapsed by the rainfall seepage, however, there is not much information for the mechanism of slope failure during rainfall. As analyzing the stability of slope by rainfall, the conventional method is to put the ground-water level on the surface of slope. However, it may provide the over-reinforcement for the slope stability. Futhermore, although over-reinforcement for the slope was fulfilled, the possibility of potential slope failure still exists. In this study, the slope stability by the conventional design method and the causes of unstable slope during rainfall were investigated. To analyze the slope stability by rainfall, the computer program SEEP/W for the analysis of seepage was used. As changing the intensity and duration of rainfall in SEEP/W, the analysis were performed. After completion of analysis, the porewater pressure data from SEEP/W was applied to SLOPE/W. As a results of this analysis, it is not reasonable that the groundwater level is going up to the surface of slope during rainfall. Therefore, the conventional reinforcement for the slope stability is not obvious to satisfy the criterion safety factor during rainfall. The reasonable counterplan is to install drainage hole on the surface of slope in order to prevent erosion and debris flow.