• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.563-574
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• 2023

Recently, climate change has affected functional responses of river basins to meteorological variables, emphasizing the importance of rainfall-runoff simulation research. Simultaneously, the growing interest in machine learning has led to its increased application in hydrological studies. However, it is not yet clear whether machine learning models are more advantageous than the conventional conceptual models. In this study, we compared the performance of the conventional GR6J model with the machine learning-based Random Forest model across 38 basins in Korea using both gauged and ungauged basin prediction methods. For gauged basin predictions, each model was calibrated or trained using observed daily runoff data, and their performance was evaluted over a separate validation period. Subsequently, ungauged basin simulations were evaluated using proximity-based parameter regionalization with Leave-One-Out Cross-Validation (LOOCV). In gauged basins, the Random Forest consistently outperformed the GR6J, exhibiting superiority across basins regardless of whether they had strong or weak rainfall-runoff correlations. This suggest that the inherent data-driven training structures of machine learning models, in contrast to the conceptual models, offer distinct advantages in data-rich scenarios. However, the advantages of the machine-learning algorithm were not replicated in ungauged basin predictions, resulting in a lower performance than that of the GR6J. In conclusion, this study suggests that while the Random Forest model showed enhanced performance in trained locations, the existing GR6J model may be a better choice for prediction in ungagued basins.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.5
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• pp.41-53
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• 2002

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation suggested by the first report of this project. According to the regions and consecutive durations, optimal design rainfalls were derived by the regional frequency analysis for L-moment in the second report of this project. Using the LH-moment ratios and Kolmogorov-Smirnov test, the optimal regional probability distribution was identified to be the Generalized extreme value (GEV) distribution among applied distributions. regional and at-site parameters of the GEV distribution were estimated by the linear combination of the higher probability weighted moments, LH-moment. Design rainfall using LH-moments following the consecutive duration were derived by the regional and at-site analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE for the design rainfall were computed and compared in the regional and at-site frequency analysis. Consequently, it was shown that the regional analysis can substantially more reduce the RRMSE, RBIAS and RR in RRMSE than at-site analysis in the prediction of design rainfall. Relative efficiency (RE) for an optimal order of L-moments was also computed by the methods of L, L1, L2, L3 and L4-moments for GEV distribution. It was found that the method of L-moments is more effective than the others for getting optimal design rainfall according to the regions and consecutive durations in the regional frequency analysis. Diagrams for the design rainfall derived by the regional frequency analysis using L-moments were drawn according to the regions and consecutive durations by GIS techniques.

• 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 Environmental Science International
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• v.21 no.12
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• pp.1511-1521
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• 2012

An attempt is made to analyse characteristic features of heavy rainfalls which occur at the metropolitan area of the Korean peninsular the on- and off- Changma season. For this, two representative heavy rainfall episodes are selected; one is the on-Changma season wherein a torrential rain episode happened at Goyang city on 12 July 2006, and the other is the off-Changma season, a heavy rainfall event in Seoul on 21 September 2006. Both recorded considerable amounts of precipitation, over 250mm in a half-day, which greatly exceeded the amount expected by numerical prediction models at those times, and caused great damage to property and life in the affected area. Similarities in the characteristics of both episodes were shown by; the location of upper-level jet streak and divergence fields of the upper wind over heavy rainfall areas, significantly high equivalent potential temperatures in the low atmospheric layer due to the entrainment of hot and humid air by the low-level jet, and the existence of very dry air and cold air pool in the middle layer of the atmosphere at the peak time of the rainfall events. Among them, differences in dynamic features of the low-level jet and the position of rainfall area along the low-level jet are remarkable.

• Journal of Wetlands Research
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• v.14 no.4
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• pp.571-580
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• 2012

This study is on the purpose of leading Geomorphoclimatic Instantaneous Unit Hydrograph(GcIUH) by using GIS Techniques, and estimating trigger rainfall for predicting flash flood in Seolmacheon catchment, mountain river watershed. This study leads GcIUH by using GIS techniques, calculates NRCS-CN values for effective rainfall rate, and analyzes 2011 main rainfall events using estimated GcIUH. According to the results, the case of Memorial bridge does not exceed the amount of threshold runoff, however, the case of Sabang bridge shows that simulated peak flow, approximately $149.4m^3/s$, exceeds the threshold runoff. To estimate trigger rainfall, this study determines the depth of 50 year-frequency designed flood amount as a threshold water depth, and estimates trigger rainfall of flash flood in consideration of duration. Hereafter, this study will analyze various flood events, estimate the appropriateness of trigger rainfall as well as threshold runoff through this analysis, and develop prototype of Flash Flood Prediction System which is considered the characteristics of mountain river watershed on the basis of this estimation.

• Journal of the Korean Geographical Society
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• v.44 no.1
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• pp.1-16
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• 2009

This study aims to classify climate zones using Empirical Orthogonal Function and clustering analyses considering both air temperature and rainfall features in South Korea. When examining climatic characteristics of air temperature and rainfall by seasons, the distribution of air temperature is affected by topography and latitude for all seasons in South Korea. The distribution of rainfall demonstrated that the Yeongdong area, the southern coastal area and Jeju island have higher rainfall while the central area in Gyeongsangbuk-do is the least rainfall area. Clustering analyses of average linkage method and Ward's method was carried out using input variables derived from principal component scores calculated through Empirical Orthogonal Function analysis for air temperature and rainfall. Ward's method showed the best result of classification of climate zones. It was well reflected effects of topography, latitude, sea, the movement of surface pressure systems, and an administrative district.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.3
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• pp.208-213
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• 2007

When overland flow water is small and slow, it moves down a stream slowly and we use it as available resource. However, it could not only be good for nothing but arouse an inundation if a lot of runoff pour down to stream at a torrential rain. So it is important to know how much water to flow out and be stored in soil and on land in order to predict a flood and conserve soil and water quality. We intended to develop the prediction model of runoff in upland at a torrential rain and conducted lysimeter study in soybean cultivation and bare soil with 3 slopeness, 3 slope length and 5 soil texture from 1985 to 1991. The data of rainfall and runoff were used when daily rainfall was over 80 mm, the level of torrential rain warning. Minimum rainfall occurring runoff (MROR) was dependent on surface coverage and slope length. However soil texture and slopeness had a little influence on MROR. Runoff after MROR increased in proportion to precipitation which depended on surface coverage, soil texture and slope. Runoff ratio was larger in fine texture and bare soil than coarse soil and soybean coverage. Runoff ratio was in proportion to a square root of slope angle(radian) and reduced with slope length to converge a certain value. From these basis, we developed the prediction model following as $$Runoff(mm)=a(s^{0.5}+l^b)(Rainfall(mm)-80(1-e^{-bl}))$$ where a is a coefficient relevant soil hydraulic properties, b is a surface coverage coefficient, s is a slope angle and l is a slope length. The coefficient a was 0.5 in sandy loam and 0.6 in clay, and b was 0.06 in bare soil and 0.5 in soybean cultivation.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1107-1119
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• 2012

The objectives of this study are to develop the flow nomograph for real-time flood forecasting and to assess its applicability in restored Cheonggye stream. The Cheonggye stream basin has the high impermeability and short concentration time and complicated hydrological characteristics. Therefore, the flood prediction method using runoff model is ineffective due to the limit of forecast. Flow nomograph which is able to forecast flood only with rainfall information. To set the forecast criteria of flow nomograph at selected flood forecast points and calculated criterion flood water level for each point, and in order to reflect various flood events set up simulated rainfall scenario and calculated rainfall intensity and rainfall duration time for each condition of rainfall. Besides, using a rating curve, determined scope of flood discharge following criterion flood water level and using SWMM model calculated flood discharge for each forecasting point. Using rainfall information following rainfall scenario calculated above and flood discharge following criterion flood water level developed flow nomograph and evaluated it by applying it to real flood event. As a result of performing this study, the applicability of flow nomograph to the basin of Cheonggye stream appeared to be high. In the future, it is reckoned to have high applicability as a method of prediction of flood of urban stream basin like Cheonggye stream.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.107-113
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• 2019

The flood forecasting model currently used in Korea calculates the runoff of basin using the lumped rainfall-runoff model and estimates the river level using the river and reservoir routing models. The lumped model assumes homogeneous drainage zones in the basin. Therefore, it can not consider various spatial characteristics in the basin. In addition, the rainfall data used in lumped model also has the same limitation because of using the point scale rainfall data. To overcome the limitations as mentioned above, many researchers have studied to apply the distributed rainfall-runoff model to flood forecasting system. In this study, to apply the Grid-based Rainfall-Runoff Model (GRM) to the Korean flood forecasting system, the optimal resolution is determined by analyzing the difference of the results of the runoff according to the various resolutions. If the grid size is to small, the computation time becomes excessive and it is not suitable for applying to the flood forecasting model. Even if the grid size is too large, it does not fit the purpose of analyzing the spatial distribution by applying the distributed model. As a result of this study, the optimal resolution which satisfies the accuracy of the bsin runoff prediction and the calculation speed suitable for the flood forecasting was proposed. The accuracy of the runoff prediction was analyzed by comparing the Nash-Sutcliffe model efficiency coefficient (NSE). The optimal resolution estimated from this study will be used as basic data for applying the distributed rainfall-runoff model to the flood forecasting system.

• The Journal of Engineering Geology
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• v.28 no.1
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• pp.47-59
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• 2018

At present, there has been a wide range of studies on debris flow in Korea, more specifically, on rainfall characteristics that trigger debris flow including rainfall intensity, rainfall duration, and preceding rainfall. the prediction of landslide / debris flow relies on the criteria for landslide watch and warning by the Korea Forest Service (KFS, 2012). Despite this, it has been found that most incidents of debris flow were caused by rainfall above the level of landslide watch, maximum hourly rainfall, extensive damage was caused even under the watch level. Under these circumstances, we calculated a rainfall triggering index (RTI) using the main factors that trigger debris flow-rainfall, rainfall intensity, and cumulative rainfall-to design a more sophisticated watch / warning criteria than those by the KFS. The RTI was classified into attention, caution, alert, and evacuation, and was assessed through the application of two debris flow incidents that occurred in Umyeon Mountain, Seoul, and Cheongju, Inje, causing serious damage and casualties. Moreover, we reviewed the feasibility of the RTI by comparing it with the KFS's landslide watch / warning criteria (KFS, 2012).