• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.398-398
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• 2015

To estimate and forecast runoff by using Aritifitial Neaural Networks model (ANNs). it has been studied in Thailand for the past 10 years. The model was developed in order to be conformed with the conditions in which the collected dataset is short and the amount of dataset is inadequate. Every year, the Northerpart of Thailand faces river overflow and flood inundation. The most important basin in this area is Yom basin. The purpose of this study is to forecast runoff at Y.14 gauge station (Si-Satchanalai district, Sukhothai province) for 3 days in advance. This station located at the upstream area of Yom River basin. Daily rainfall and daily runoff from Royal Irrigation Department and Meteorological Department during flood period 2000-2012 were used as input data. In order to check an accuracy of forecasting, forecasted runoff were compared with observed data by pursuing Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination ($R^2$). The result of the first day gets the highest accuracy and then decreased in day 2 and day 3, consequently. NSE and $R^2$ values for frist day of runoff forecasting is 0.76 and 0.776, respectively. On the second day, those values are 0.61 and 0.65, respectively. For the third day, the aforementioned valves are 0.51 and 0.52, respectively. The results confirmed that the ANNs model can be used when the range of collected dataset is short and insufficient. In conclusion, the ANNs model is suitable for applying during flood incident because it is easy to use and does not require numerous parameters for simulating.

• Journal of Korea Water Resources Association
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• v.44 no.11
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• pp.853-861
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• 2011

The objective of this study is to assess Sejong University River Forecast (SURF) model which consists of a continuous rainfall-runoff model and measured streamflow assimilation using ensemble Kalman filter technique for streamflow forecast on Nakdong river basin. The study area is divided into 43 subbasins. The forecasted streamflows are evaluated at 12 measurement sites during flood season from 2006 to 2007. The forecasted ones are improved due to the impact of the measured streamflows assimilation. In effectiveness indices corresponding to 1~5 h forecast lead times, the accuracy of the forecasted streamflows with the assimilation approach is improved by 46.2~30.1% compared with that using only the rainfall-runoff model. The mean normalized absolute error of forecasted peak flow without and with data assimilation approach in entering 50% of the measured rainfall, respectively, the accuracy of the latter is improved about 40% than that of the former. From these results, SURF model is able to be used as a real-time river forecast model.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.1061-1072
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• 2005

A hydrological grey model is developed to forecast short-term river runoff from the Naju watershed located at upstream of the Youngsan estuary dam in Korea. The runoff of the Naju watershed is measured in real time at the Naju streamflow gauge station, which is a key station for forecasting the upstream inflow and operating the gates of the estuary dam in flood period. The model's governing equation is formulated on the basis of the grey system theory. The model parameters are reparameterized in combination with the grey system parameters and estimated with the annealing-simplex method In conjunction with an objective function, HMLE. To forecast accurately runoff, the fifth order differential equation was adopted as the governing equation of the model in consideration of the statistic values between the observed and forecast runoff. In calibration, RMSE values between the observed and simulated runoff of two and six Hours ahead using the model range from 3.1 to 290.5 $m^{3}/s,\;R^2$ values range from 0.909 to 0.999. In verification, RMSE values range from 26.4 to 147.4 $m^{3}/s,\;R^2$ values range from 0.940 to 0.998, compared to the observed data. In forecasting runoff in real time, the relative error values with lead-time and river stage range from -23.4 to $14.3\%$ and increase as the lead time increases. The results in this study demonstrate that the proposed model can reasonably and efficiently forecast runoff for one to six Hours ahead.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.535-538
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• 2003

A Grey model was developed to forecast short-term runoff from the Naju watershed in Korea. In calibration, the root mean square error(RMSE) of the simulated runoff of six hours ahead using Grey model ranged from 6.3 to $290.52m^3/s,\;R^2$ ranged from 0.91 to 0.99, compared to the observed data. In verification, the RMSE ranged from 75.7 to $218.9m^3/s,\;R^2$ ranged from 0.87 to 0.96, compared to the observed data. The results in this study demonstrate that the proposed model can reasonably forecast runoff one to six hours ahead.

• Atmosphere
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• v.30 no.2
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• pp.155-167
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• 2020

In order to produce more detailed and accurate information of river discharge and freshwater discharge, global high-resolution hydrodynamic model (CaMa-Flood) is applied to an operational land surface model of global seasonal forecast system. In addition, bias correction to grid runoff for the hydrodynamic model is attempted. CaMa-Flood is a river routing model that distributes runoff forcing from a land surface model to oceans or inland seas along continentalscale rivers, which can represent flood stage and river discharge explicitly. The runoff data generated by the land surface model are bias-corrected by using composite runoff data from UNH-GRDC. The impact of bias-correction on the runoff, which is spatially resolved on 0.5° grid, has been evaluated for 1991~2010. It is shown that bias-correction increases runoff by 30% on average over all continents, which is closer to UNH-GRDC. Two experiments with coupled CaMa-Flood are carried out to produce river discharge: one using this bias correction and the other not using. It is found that the experiment adapting bias correction exhibits significant increase of both river discharge over major rivers around the world and continental freshwater discharge into oceans (40% globally), which is closer to GRDC. These preliminary results indicate that the application of CaMa-Flood as well as bias-corrected runoff to the operational global seasonal forecast system is feasible to attain information of surface water cycle from a coupled suite of atmospheric, land surface, and hydrodynamic model.

• 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 Satellite, Information and Communications
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• v.12 no.3
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• pp.22-34
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• 2017

The K-DRUM(K-water hydrologic & hydraulic Distributed RUnoff Model), a distributed rainfall-runoff model of K-water, calculates predicted runoff and water surface level of a dam using precipitation data. In order to obtain long-term hydrometeorological information, K-DRUM requires long-term weather forecast. In this study, we built a system providing long-term hydrometeorological information using predicted rainfall ensemble of GloSea5(Global Seasonal Forecast System version 5), which is the seasonal meteorological forecasting system of KMA introduced in 2014. This system produces K-DRUM input data by automatic pre-processing and bias-correcting GloSea5 data, then derives long-term inflow predictions via K-DRUM. Web-based UI was developed for users to monitor the hydrometeorological information such as rainfall, runoff, and water surface level of dams. Through this UI, users can also test various dam management scenarios by adjusting discharge amount for decision-making.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.259-259
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• 2016

This study examined Artificial Neurons Networks model (ANNs) for forecast flash discharge at Southern part of Thailand by using rainfall data and discharge data. The Sungai Kolok River Basin has meant the border crossing between Thailand and Malaysia which watershed drains an area lies in Thailand 691.88 square kilometer from over all 2,175 square kilometer. The river originates in mountainous area of Waeng district then flow through Gulf of Thailand at Narathiwat Province, which the river length is approximately 103 kilometers. Almost every year, flooding seems to have increased in frequency and magnitude which is highly non-linear and complicated phenomena. The purpose of this study is to forecast runoff on Sungai Kolok at X.119A gauge station (Sungai Kolok district, Narathiwat province) for 3 days in advance by using Artificial Neural Networks model (ANNs). 3 daily rainfall stations and 2 daily runoff station have been measured by Royal Irrigation Department and Meteorological Department during flood period 2000-2014 were used as input data. In order to check an accuracy of forecasting, forecasted runoff were compared with observed data by pursuing Coefficient of determination ($R^2$). The result of the first day gets the highest accuracy and then decreased in day 2 and day 3, consequently. $R^2$values for first day, second day and third day of runoff forecasting is 0.71, 0.62 and 0.49 respectively. The results confirmed that the ANNs model can be used when the range of collected dataset is short and real-time operated. In conclusion, the ANNs model is suitable to runoff forecasting during flood incident of Sungai Kolok river because it is straightforward model and require with only a few parameters for simulation.

• 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).

• Journal of Korea Water Resources Association
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• v.41 no.7
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• pp.737-746
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• 2008

The purpose of developing a rainfall-runoff and reservoir model is to provide an analysis tool for hydrological engineers in order to forecast discharge of rivers and to accomplish reservoir operations easily and accurately. In this study, based on the short-term rainfall-runoff storage function model which has gained popularity for real time flood forecast in practical water management affairs, a long-term runoff model was developed for the improvement of the calculation method of effective rainfall and percolation at the infiltration area. Annual discharge was simulated for three dam watersheds(Andong, Hapcheon, Milyang) in Nakdong River basin to analyze the accuracy of the developed model and compare it to SSARR model, which is used as the long-term runoff model in current practical water management affairs. As the result of the comparison of hydrographs, SSARR model showed relatively better results. However, it is possible for the developed model to simulate reliable long-term runoff using relatively little available data and is useful for hydrological engineers in practical affairs.