• Korean Journal of Ecology and Environment
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• v.43 no.3
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• pp.349-355
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• 2010

This study aims at the augmentation of reliability of the long-term rainfall runoff model. To do so agricultural water uses are evaluated by analyzing the effects of small scale irrigational hydraulic structures on long term runoff processes and thereby rainfall-runoff model is modified considering them. As a result the simulation results of the sub-basins having more agricultural reservoirs than the others are disagreed with the observations. The 2nd quarter simulation results show similar trend to it. Especially the farming seasonal results of the drought year as the year of 2008 have many negative discharge values due to the lack of agricultural water uses. This result come from the water uses input data corresponding to not real water uses but water demands. In this study the formulas are derived to estimate the discharges and return ratios and the long term rainfall-runoff model is reformulated based on these. It is confirmed that the errors of the simulation results could be reduced by considering the effects of small scale irrigational hydraulic structures and the reliability of the simulation results improved greatly.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.4
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• pp.97-107
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• 2017

This study is to determine the coefficients of regression equations and to select the optimal regression equation in the LOADEST model after classifying the whole study period into 5 flow conditions for 16 watersheds located in the Nakdonggang waterbody. The optimized coefficients of regression equations were derived using the gradient descent method as a learning method in Tensorflow which is the engine of machine-learning method. In South Korea, the variability of streamflow is relatively high, and rainfall is concentrated in summer that can significantly affect the characteristic analysis of pollutant loads. Thus, unlike the previous application of the LOADEST model (adjusting whole study period), the study period was classified into 5 flow conditions to estimate the optimized coefficients and regression equations in the LOADEST model. As shown in the results, the equation #9 which has 7 coefficients related to flow and seasonal characteristics was selected for each flow condition in the study watersheds. When compared the simulated load (SS) to observed load, the simulation showed a similar pattern to the observation for the high flow condition due to the flow parameters related to precipitation directly. On the other hand, although the simulated load showed a similar pattern to observation in several watersheds, most of study watersheds showed large differences for the low flow conditions. This is because the pollutant load during low flow conditions might be significantly affected by baseflow or point-source pollutant load. Thus, based on the results of this study, it can be found that to estimate the continuous pollutant load properly the regression equations need to be determined with proper coefficients based on various flow conditions in watersheds. Furthermore, the machine-learning method can be useful to estimate the coefficients of regression equations in the LOADEST model.