• Journal of the Korean Professional Engineers Association
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• v.18 no.2
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• pp.1-5
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• 1985

In the flood prediction research, it is pointed out that the difficulty of flood prediction is the frequently experienced overestimation of flood peak. That is caused by the rainfall prediction difficulty and the nonlinearity of hydrological phenomena. Even though the former reason will remain still unsolved, but the latter one can be possibly resolved the method of the AMRA (Auto Regressive Moving Average) model for each runoff component as developed by Dr. Hino and Dr. Hasebe. The principle of the method consists of separating though the numerical filters the total runoff time series into long-term, intermediate and short-term components, or ground water flow, interflow, and surface flow components. As a total system, a hydrological system is a non-linear one. However, once it is separated into two or three subsystems, each subsystem may be treated as a linear system. Also the rainfall components into each subsystem a estimated inversely from the runoff component which is separated from the observed flood. That is why flood prediction can be done without rainfall data. In the prediction of surface flow, the Kalman filter will be applicable but this paper shows only impulse function method.

• Journal of Korea Water Resources Association
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• v.44 no.12
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• pp.941-954
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• 2011

In this study, past method and recent method for flood discharge with domestic multi-purpose dams in Korea were compared and analyzed with respect to the scale of watershed. Rainfall depth, temporal pattern, rainfall excess, rainfall-runoff model, parameter estimation and base flow were selected as the principal factors affecting flood discharge and effects on flood discharge were analyzed quantitatively by using sensitivity analysis. The results showed that the flood discharges calculated by past and recent method increased and decreased with a wide range of discharge with respect to the scale of watershed. The reason for decrease of flood discharge is the exchange of temporal pattern of rainfall and the principal reasons for increase of flood discharge are the increase of rainfall depth by unusual weather phenomena and the difference of estimation method for parameters of unit hydrograph.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.166-166
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• 2020

Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1149-1159
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• 2018

This study aims to predict the future flood damage cost of 113 middle range watersheds using 26 GCM outputs, hourly maximum rainfall, 10-min maximum rainfall, number of days of 80 mm/day, daily rainfall maximum, annual rainfall amount, DEM, urbanization ratio, population density, asset density, road improvement ratio, river improvement ratio, drainage system improvement ratio, pumping capacity, detention basin capacity and previous flood damage costs. A constrained multiple linear regression model was used to construct the relationships between the flood damage cost and other variables. Future flood damage costs were estimated for different RCP scenarios such as 4.5 and 8.5. Results demonstrated that rainfall related factors such as annual rainfall amount, rainfall extremes etc. widely increase. It causes nationwide future flood damage cost increase. Especially the flood damage cost for Eastern part watersheds of Kangwondo and Namgang dam area may mainly increase.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.191-191
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• 2017

Floods have become more widespread and frequent among natural disasters and consisted significant losses of lives and properties worldwide. Flood's impacts are threatening socio-economic and people's lives in the Mekong River Basin every year. The objective of this study is to identify the flood hazard areas and inundation depth in the Mekong River Basin. A rainfall-runoff and flood inundation model is necessary to enhance understanding of characteristic of flooding. Rainfall-Runoff-Inundation (RRI) model, a two-dimensional model capable of simulating rainfall-runoff and flood inundation simultaneously, was applied in this study. HydoSHEDS Topographical data, APPRODITE precipitation, MODIS land use, and river cross section were used as input data for the simulation. The Shuffled Complex Evolution (SCE-UA) global optimization method was integrated with RRI model to calibrate the sensitive parameters. In the present study, we selected flood event in 2000 which was considered as 50-year return period flood in term of discharge volume of 500 km3. The simulated results were compared with observed discharge at the stations along the mainstream and inundation map produced by Dartmouth Flood Observatory and Landsat 7. The results indicated good agreement between observed and simulated discharge with NSE = 0.86 at Stung Treng Station. The model predicted inundation extent with success rate SR = 67.50% and modified success rate MSR = 74.53%. In conclusion, the RRI model was successfully used to simulate rainfall runoff and inundation processes in the large scale Mekong River Basin with a good performance. It is recommended to improve the quality of the input data in order to increase the accuracy of the simulation result.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.839-850
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• 2018

The flood damage in urban areas due to torrential rain is increasing with urbanization. For this reason, accurate and rapid flooding forecasting and expected inundation maps are needed. Predicting the extent of flooding for certain rainfalls is a very important issue in preparing flood in advance. Recently, government agencies are trying to provide expected inundation maps to the public. However, there is a lack of quantifying the extent of inundation caused by a particular rainfall scenario and the real-time prediction method for flood extent within a short time. Therefore the real-time prediction of flood extent is needed based on rainfall-runoff-inundation analysis. One/two dimensional model are continued to analyize drainage network, manhole overflow and inundation propagation by rainfall condition. By applying the various rainfall scenarios considering rainfall duration/distribution and return periods, the inundation volume and depth can be estimated and stored on a database. The Rainfall-Duration-Flooding Quantity (RDF) relationship curve based on the hydraulic analysis results and the Self-Organizing Map (SOM) that conducts unsupervised learning are applied to predict flooded area with particular rainfall condition. The validity of the proposed methodology was examined by comparing the results of the expected flood map with the 2-dimensional hydraulic model. Based on the result of the study, it is judged that this methodology will be useful to provide an unknown flood map according to medium-sized rainfall or frequency scenario. Furthermore, it will be used as a fundamental data for flood forecast by establishing the RDF curve which the relationship of rainfall-outflow-flood is considered and the database of expected inundation maps.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.267-278
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• 2022

Recently Flood damage volume has increased as heavy rain has frequently occurred. Especially urban areas are a vulnerability to flooding damage because of densely concentrated population and property. A local government is preparing to mitigate flood damage through the heavy rain warning issued by Korea Meteorological Administration. This warning classification is identical for a national scale. However, Seoul has 25 administrative districts with different regional characteristics such as climate, topography, disaster prevention state, and flood damage severity. This study considered the regional characteristics of 25 administrative districts to analyze the flood vulnerability using entropy weight and Euclidean distance. The rainfall classification was derived based on probability rainfall and flood damage rainfall that occurred in the past. The result shows the step 2 and step 4 of rainfall classification was not significantly different from the heavy rain classification of the Korea Meteorological Administration. The flood vulnerability is high with high climate exposure and low adaptability to climate change, and the rainfall classification is low in the northern region of Seoul. It is possible to preemptively respond to floods in the northern region of Seoul based on relatively low rainfall classification. In the future, we plan to review the applicability of rainfall forecast data using the rainfall classification of results from this study. These results will contribute to research for preemptive flood response measures.

• Water Engineering Research
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• v.4 no.4
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• pp.203-214
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• 2003

The severe flood disaster by a typoon Rusa was occurred in the last year in Korea. The Rusa brought the rainfall of 870.5mm per a day in the city of Kangnung, Kangwon-do, Korea and this rainfall amount is 62% of the annual mean rainfall in this area. Our focus is to investigate the flash flood guidance and the sediment yield for the basins of small streams of Yangyang town in Kangnung area. Say, the flash flood guidance and the sediment yield by the Rusa are estimated and compared with the given informations obtained from the past flood events. As the results, the flash flood guidance and sediment yield in the study area showed much bigger values than the given informations and so we could know that the Rusa influenced the severe flood of the study area.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.188-188
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• 2017

Rainfall is an important input to hydrological models. The accuracy of hydrological studies for water resources and floods management depend primarily on the estimation of rainfall. Thailand is among the countries that have regularly affected by floods. Flood forecasting and warning are necessary to prevent or mitigate loss and damage. Merging near real time satellite-based precipitation estimation with relatively high spatial and temporal resolutions to ground gauged precipitation data could contribute to reducing uncertainty and increasing efficiency for flood forecasting application. This study tested the applicability of satellite-based rainfall for water resources management and flood forecasting. The objectives of the study are to assess uncertainty associated with satellite-based rainfall estimation, to perform bias correction for satellite-based rainfall products, and to evaluate the performance of the bias-corrected rainfall data for the prediction of flood events. This study was conducted using a case study of Thai catchments including the Chao Phraya, northeastern (Chi and Mun catchments), and the eastern catchments for the period of 2006-2015. Data used in the study included daily rainfall from ground gauges, telegauges, and near real time satellite-based rainfall products from TRMM, GSMaP and PERSIANN CCS. Uncertainty in satellite-based precipitation estimation was assessed using a set of indicators describing the capability to detect rainfall event and efficiency to capture rainfall pattern and amount. The results suggested that TRMM, GSMaP and PERSIANN CCS are potentially able to improve flood forecast especially after the process of bias correction. Recommendations for further study include extending the scope of the study from regional to national level, testing the model at finer spatial and temporal resolutions and assessing other bias correction methods.