• The Korean Journal of Quaternary Research
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• v.19 no.2
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• pp.74-79
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• 2005

Recently, several attempts have been made to provide reasonable information on unusual severe weather phenomena such as tolerant heavy rains and very wild typhoons. Quantitative precipitation forecasts and probabilistic quantitative precipitation forecasts (QPFs and PQPFs, respectively) might be one of the most promising methodologies for early warning on the flesh floods because those diagnostic precipitation models require less computational resources than fine-mesh full-dynamics non-hydrostatic mesoscale model. The diagnostic rainfall model used in this study is the named QPM(Quantitative Precipitation Model), which calculates the rainfall by considering the effect of small-scale topography which is not treated in the mesoscale model. We examine the capability of probabilistic diagnostic rainfall model in terms of how well represented the observed several rainfall events and what is the most optimistic resolution of the mesoscale model in which diagnostic rainfall model is nested. Also, we examine the integration time to provide reasonable fine-mesh rainfall information. When we apply this QPM directly to 27 km mesh meso-scale model (called as M27-Q3), it takes about 15 min. while it takes about 87 min. to get the same resolution precipitation information with full dynamic downscaling method (called M27-9-3). The quality of precipitation forecast by M27-Q3 is quite comparable with the results of M27-9-3 with reasonable threshold value for precipitation. Based on a series of examination we may conclude that the proosed QPM has a capability to provide fine-mesh rainfall information in terms of time and accuracy compared to full dynamical fine-mesh meso-scale model.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1142-1146
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• 2009

A rainfall simulation and forecasting technique that can generate daily rainfall sequences conditional on multi-model ensemble GCMs is developed and applied to data in Korea for the major rainy season. The GCM forecasts are provided by APEC climate center. A Weather State Based Downscaling Model (WSDM) is used to map teleconnections from ocean-atmosphere data or key state variables from numerical integrations of Ocean-Atmosphere General Circulation Models to simulate daily sequences at multiple rain gauges. The method presented is general and is applied to the wet season which is JJA(June-July-August) data in Korea. The sequences of weather states identified by the EM algorithm are shown to correspond to dominant synoptic-scale features of rainfall generating mechanisms. Application of the methodology to seasonal rainfall forecasts using empirical teleconnections and GCM derived climate forecast are discussed.

• Journal of Environmental Science International
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• v.22 no.3
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• pp.303-308
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• 2013

This paper proposed a new method for estimating missing values in time series rainfall data. The proposed method integrated the two most widely used estimation methods, general linear model(GLM) and ordinary kriging(OK), by taking a weighted average of covariance matrices derived from each of the two methods. The proposed method was cross-validated using daily rainfall data at thirteen rain gauges in the Hyeong-san River basin. The goodness-of-fit of the proposed method was higher than those of GLM and OK, which can be attributed to the weighting algorithm that was designed to minimize errors caused by violations of assumptions of the two existing methods. This result suggests that the proposed method is more accurate in missing values in time series rainfall data, especially in a region where the assumptions of existing methods are not met, i.e., rainfall varies by season and topography is heterogeneous.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3B
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• pp.269-279
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• 2009

The design flood estimation in a large river basin has a lot of uncertainties in areal reduction factors, time-spatial rainfall distribution, and parameters of rainfall-runoff model. The use of historical concurrent rainfall events for estimating design flood would reduce the uncertainties. This study presents a procedure for estimating design floods using historical rainfall events and storage function model. The design rainfall and time-spatial distribution were determined through analyzing concurrent rainfall events, and the design floods were estimated using storage function model with a non-linear hydrology response. To evaluate the applicability of the procedure of this study, the estimated floods were compared to results of frequency analysis of flood data. Both floods gave very similar results. It shows the applicability of the procedure presented in this study for estimating design floods in practices.

• Journal of Korea Water Resources Association
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• v.48 no.11
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• pp.925-936
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• 2015

The purpose of this study is to explore the effectiveness of dual-polarization radar rainfall by comapring with the flood inundation record map through KIMSTORM(Grid-based KIneMatic wave STOrm Runoff Model). For Namgang dam ($2,293km^2$) watershed, the Bisl dual-polarization radar data for 3 typhoons (Khanun, Bolaven, Sanba) and 1 heavy rain event in 2012 were prepared. For both 28 ground rainfall data and radar rainfall data, the model was calibrated using observed discharge data at 5 stations with $R^2$, Nash and Sutcliffe Model Efficiency (ME) and Volume Conservation Index (VCI). The calibration results of $R^2$, ME and VCI were 0.85, 0.78 and 1.09 for ground rainfall and 0.85, 0.79, and 1.04 for radar rainfall respectively. The flood inundation record areas (SY and MD/SG district) by typhoon Sanba were compared with the distributed modeling results. The spatial distribution by radar rainfall produced more surface runoff from the watershed and simulated higher stream discharge than the ground rainfall condition in both SY and MD/SG district. In case of MD/SG district, the stream water level by radar rainfall near the flood inundation area showed 0.72 m higher than the water level by ground rainfall.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.6
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• pp.748-757
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• 2004

In this study, we analyzed the sensitivity of parameters which affect the result of ILLUDAS model, in the various rainfall conditions. The three basins including Namgaja, Kings creek, Gray haven were selected for this research. The rainfall conditions are considered in terms of the rainfall frequency, the duration and the distribution. In most cases, the impermeability area ratio, the sewer slope, and the sewer roughness coefficient give more significant effects on the results than others. The results show that as increasing the rainfall frequency, the sensitivity of the parameters, sewer slope and roughness coefficient are rised, while the impermeability area ratio is decreasing. And also, for the duration of rainfall, the impermeability area ratio's sensitivity shows similar tendency. In case of the rainfall distribution, the parameters of the sewer roughness and the impermeability area ratio show more sensitive in Huff distribution. Especially, The impermeability area ratio is the most sensitive parameter in Central blocking and Yen & Chow distributions respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.69-79
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• 2017

In this study, we developed real-time urban stream discharge forecasting model using short-term rainfall forecasts data simulated by a regional climate model (RCM). The National Centers for Environmental Prediction (NCEP) Climate Forecasting System (CFS) data was used as a boundary condition for the RCM, namely the Global/Regional Integrated Model System(GRIMs)-Regional Model Program (RMP). In addition, we make ensemble (ESB) forecast with different lead time from 1-day to 3-day and its accuracy was validated through temporal correlation coefficient (TCC). The simulated rainfall is compared to observed data, which are automatic weather stations (AWS) data and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA 3B43; 3 hourly rainfall with $0.25^{\circ}{\times}0.25^{\circ}$ resolution) data over midland of Korea in July 26-29, 2011. Moreover, we evaluated urban rainfall-runoff relationship using Storm Water Management Model (SWMM). Several statistical measures (e.g., percent error of peak, precent error of volume, and time of peak) are used to validate the rainfall-runoff model's performance. The correlation coefficient (CC) and the Nash-Sutcliffe efficiency (NSE) are evaluated. The result shows that the high correlation was lead time (LT) 33-hour, LT 27-hour, and ESB forecasts, and the NSE shows positive values in LT 33-hour, and ESB forecasts. Through this study, it can be expected to utilizing the real-time urban flood alert using short-term weather forecast.

• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.7-14
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• 2006

The radar relationship was estimated for the selected rainfall event at Yeongchun station within Chungjudam basin where the discharge record was the range of from 1,000 CMS to 9,000 CMS. By calibrating the rainfall coefficient parameter estimated by radar relationship in small hydrology basin, rainfall with the topography properties was calculated. Three different rainfall estimation methods were compared:(1) radar relationship method (2) Thiessen method (3) Isohyetal method (4) Inverse distance method. Basin model was built by applying HEC-GeoHMS which uses digital elevation model to extract hydrological characteristic and generate river network. The proposed basin model was used as an input to HEC-HMS to build a runoff model. The runoff estimation model applying radar data showed the good result. It is proposed that the radar data would produce more rapid and accurate runoff forecasting especially in the case of the partially concentrated rainfall due to the atmospheric change. The proposed radar relationship could efficiently estimate the rainfall on the study area(Chungjudam basin).