• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.976-976
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• 2012

The applicability of a distributed rainfall-runoff model for large river basin flood forecasts is analyzed by applying the model to the Nakdong River basin. The spatially explicit hydrologic model was constructed and calibrated by the several storm events. The assimilation of the large scale Nakdong River basin were conducted by calibrating the sub-basin channel outflow, dam discharge in the basin rainfall-runoff model. The applicability of automatic and semi-automatic calibration methods was analyzed for real time calibrations. Further an ensemble distributed rainfall runoff model has been developed to measure the runoff hydrograph generated for any temporally-spatially varied rainfall events, also the runoff of basin can be forecast at any location as well. The results of distributed rainfall-runoff model are very useful for flood managements on the large scale basins. That offer facile, realistic management method for the avoiding the potential flooding impacts and provide a reference for the construct and developing of flood control facilities.

• Journal of Environmental Science International
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• v.12 no.9
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• pp.949-955
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• 2003

The subject basin of the research was the basin of Yeongcheon Dam located in the upper reaches of the Kumho River. The parameters of the model were derived from the results of abstracting topological properties out of rainfall-runoff observation data about heavy rains and Digital Elevation Modeling(DEM) materials. This research aimed at suggesting the applicability of the CELLMOD Model, a distribution-type model, in interpreting runoff based on the topological properties of a river basin, by carrying out runoff interpretation far heavy rains using the model. To examine the applicability of the model, the calculated peaking characteristics in the hydrograph was analyzed in comparison with observed values and interpretation results by the Clark Model. According to the result of analysis using the CELLMOD Model proposed in the present research for interpreting the rainfall-runoff process, the model reduced the physical uncertainty in the rainfall-runoff process, and consequently, generated improved results in forecasting river runoff. Therefore it was concluded that the algorithm is appropriate for interpreting rainfall-runoff in river basins. However, to enhance accuracy in interpreting rainfall-runoff it is necessary to supplement heavy rain patterns in subject basins and to subdivide a basin into minor basins for analysis. In addition, it is necessary to apply the model to basins that have sufficient observation data, and to identify the correlation between model parameters and the basin characteristics(channel characteristics).

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.93-98
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• 2001

This study was performed to analyse the rainfall and the rainfall-runoff characteristics of a rural watershed. The Sangwha basin($105.9km^{2}$) in the Geum river system was selected for this study. The arithmetic mean method, the Thiessen's weighing method, and the isohyetal method were used to analyse areal rainfall distribution and the Huff's quartile method was used to analyse temporal rainfall distribution. In addition, daily runoff analyses were peformed using the DAWAST and tank model. In the model calibration, the data from June through November, 1999 were used. In the model calibration, the observed runoff depth was 513.7mm and runoff rate was 45.2%, and the DAWAST model simulated runoff depth was 608.6mm and runoff rate was 53.5%, and the tank model runoff depth was 596.5mm and runoff rate was 52.5%, respectively. In the model test, the data from June through November, 2000 were used. In the model test, the observed runoff depth was 1032.3mm and runoff rate was 72.5%, and the DAWAST model simulated runoff depth was 871.6mm and runoff rate was 61.3%, and the tank model runoff depth was 825.4mm and runoff rate was 58%, respectively. The DAWAST and tank model's $R^{2}$ and RMSE were 0.85, 3.61mm, and 0.85, 2.77mm in 1999, and 0.83, 5.73mm, and 0.87, 5.39mm in 2000, respectively. Both models predicted low flow runoff better than flood runoff.

• 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.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.2
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• pp.45-57
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• 2003

An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes were optimized using total error and Bayesian information criterion. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$is greater than 0.99) for calibration and verification data sets. Increasing the time horizon for application data sets, thus mating the model suitable for flood forecasting. decreases the accuracy of the model. The resulting optimal EBPN models for forecasting hourly runoff consists of ten rainfall and four runoff data(ANN0410 model) and ten rainfall and ten runoff data(ANN1010 model). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$is greater than 0.92).

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.439-439
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• 2015

The temporal and spatial relationship of the weather elements such as rainfall and temperature is closely linked to the streamflow simulation, especially, to the flood forecasting problems. For the study area, Imjin river basin, which has the specific characteristics in geography with river cross operation between North and South Korea, the meteorological information in the northern area is totally deficiency, lead to the inaccuracy of streamflow estimation. In the paper, this problem is solved by using the combination of global (such as soil moisture content, land use) and local hydrologic components data such as weather data (precipitation, evapotranspiration, humidity, etc.) for the model-driven runoff (surface flow, lateral flow and groundwater flow) data in each subbasin. To compute the streamflow in Imjin river basin, this study is applied the hydrologic model SURR (Sejong Univ. Rainfall-Runoff) which is the continuous rainfall-runoff model used physical foundations, originally based on Storage Function Model (SFM) to simulate the intercourse of the soil properties, weather factors and flow value. The result indicates the spatial variation in the runoff response of the different subbasins influenced by the input data. The dependancy of runoff simulation accuracy depending on the qualities of input data and model parameters is suggested in this study. The southern region with the dense of gauges and the adequate data shows the good results of the simulated discharge. Eventually, the application of SURR model in Imjin riverbasin gives the accurate consequence in simulation, and become the subsequent runoff for prediction in the future process.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.3
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• pp.281-288
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• 2004

The objective of this study is to propose a methodology of the flood runoff analysis in steep mountainous basins and the analysis basin is the Jasa valley basin in Chungju city Analyzing the spatial pattern of the rainfall in 1994. 6 30~7.1, the seasonal rainy front was tied up in the whole central district, and the rainfall center was moving from the northern Chungbuk province to the northern Kyongbuk province and caused heavy storm. Analyzing the temporal pattern with the Huff method, the 52.5% of the rainfall was concentrated on the 3rd quartile. Rainfall frequency analysis is accomplished by five distribution types; 2-parameter Lognomal, 3-parameter Lognomal, Pearson Type III, Log-Pearson Type III and Extremal Type I distribution Rainfall-runoff analysis in Jasa valley basin was made using HEC-HMS model. Jasa valley basin was divided into 3 sub-basins and the analysis point was 3 points{A, B and C point) With the rainfall data measured by the 10 minutes, the flood runoff also was calculated by as many minutes. SCS CN model, Clark UH model and Muskingum routing model in HEC-HMS model were used to simulate the runoff volume using selected rainfall event.

• Journal of Korea Water Resources Association
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• v.52 no.9
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• pp.603-613
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• 2019

This study proposes a method to generate runoff ensemble members using a rainfall-runoff model based on the shot noise process (hereafter the rainfall-runoff model). The proposed method was applied to generate runoff ensemble members for three drainage basins of Daerim 2, Guro 1 and the Jungdong, whose results were then compared with the observed. The parameters of the rainfall-runoff model were estimated using the empirical formulas like the Kerby, Kraven II and Russel, also the concept of modified rational formula. Gamma and exponential distributions were used to generate random numbers of the parameters of the rainfall-runoff model. Especially, the gamma distribution is found to be useful to generate various random numbers depending on the pre-assigned relationship between mean and standard deviation. Comparison between the generated runoff ensemble members and the observed shows that those runoff ensemble members generated using the gamma distribution with its standard deviation twice of the mean properly cover the observed runoff.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.208-208
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• 2022

Recently, the frequency of abnormal weather due to complex factors such as global warming is increasing frequently. From the past rainfall patterns, it is evident that climate change is causing irregular rainfall patterns. This phenomenon causes difficulty in predicting rainfall and makes it difficult to prevent and cope with natural disasters, casuing human and property damages. Therefore, accurate rainfall estimation and rainfall occurrence time prediction could be one of the ways to prevent and mitigate damage caused by flood and drought disasters. However, rainfall prediction has a lot of uncertainty, so it is necessary to understand and reduce this uncertainty. In addition, when accurate rainfall prediction is applied to the rainfall-runoff model, the accuracy of the runoff prediction can be improved. In this regard, this study aims to increase the reliability of rainfall prediction by analyzing the uncertainty of the Korean rainfall ensemble prediction data and the outflow analysis model using the Limited Area ENsemble (LENS) and the Grid based Rainfall-runoff Model (GRM) models. First, the possibility of improving rainfall prediction ability is reviewed using the QM (Quantile Mapping) technique among the bias correction techniques. Then, the GRM parameter calibration was performed twice, and the likelihood-parameter applicability evaluation and uncertainty analysis were performed using R², NSE, PBIAS, and Log-normal. The rainfall prediction data were applied to the rainfall-runoff model and evaluated before and after calibration. It is expected that more reliable flood prediction will be possible by reducing uncertainty in rainfall ensemble data when applying to the runoff model in selecting behavioral models for user uncertainty analysis. Also, it can be used as a basis of flood prediction research by integrating other parameters such as geological characteristics and rainfall events.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.3
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• pp.3-13
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• 2005

The conceptual rainfall-runoff models are used to predict complex hydrological effects of a basin. However, to obtain reliable results, there are some difficulties and problems in choosing optimum model, calibrating, and verifying the chosen model suitable for hydrological characteristics of the basin. In this study, Genetic Algorithm and SCE-UA method as global optimization methods were applied to compare the each optimization technique and to analyze the application for the rainfall-runoff models. Modified TANK model that is used to calculate outflow for watershed management and reservoir operation etc. was optimized as a long term rainfall-runoff model. And storage-function model that is used to predict real-time flood using historical data was optimized as a short term rainfall-runoff model. The optimized models were applied to simulate runoff on Pyeongchang-river watershed and Bocheong-stream watershed in 2001 and 2002. In the historical data study, the Genetic Algorithm and the SCE-UA method showed consistently good results considering statistical values compared with observed data.