• Environmental Engineering Research
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• v.15 no.2
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• pp.123-126
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• 2010

Modeling non-point pollution across multiple scales has become an important environmental issue. As a more representative and practical approach in quantifying and qualifying surface water, a modular neural network (MNN) was implemented in this study. Two different site-scales ($1.5\;{\times}\;10^5$ and $1.62\;{\times}\;10^6\;m^2$) with the same plants, soils, and paddy field management practices, were selected. Hydrologic data (rainfall, irrigation and surface discharge) and water quality data (time-series nutrient loadings) were continuously monitored and then used for the verification of MNN performance. Correlation coefficients (R) for the results predicted from the networks versus measured values were within the range of 0.41 to 0.95. The small block could be extrapolated to the large field for the rainfall-surface drainage process. Nutrient prediction produced less favorable results due to the complex phenomena of nutrients in the drainage water. However, the feasibility of using MNN to generate improved prediction accuracy was demonstrated if more hydrologic and environmental data are provided. The study findings confirmed the estimation accuracy of the upscaling from a small-segment block to large-scale paddy field, thereby contributing to the establishment of water quality management for sustainable agriculture.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.739-745
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• 2018

Recently, the incidence of heavy rainfall is increasing. Therefore, a rainfall analysis should be performed considering increasing frequency. The current rainfall analysis for hydrologic design use the hourly rainfall data of ASOS with a density of 36 km on the Korean Peninsula. Therefore, medium and small scale watershed included Thiessen network at the same rainfall point are analyzed with the same design rainfall and time distribution. This causes problem that the watershed characteristics can not be considered. In addition, there is a problem that the temporal-spatial change of the heavy rainfall occurring in the range of 10~20 km can not be considered. In this study, Author estimated design rainfall considering heavy rainfall using minutely rainfall data of AWS, which are relatively dense than ASOS. Also, author analyzed the time distribution and runoff of each case to estimate the huff's method suitable for the watershed. The research result will contribute to the estimation of the design hydrologic data considering the heavy rainfall and watershed characteristics.

• Journal of Korean Society on Water Environment
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• v.25 no.3
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• pp.351-362
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• 2009

The Soil and Water Assessment Tool (SWAT) model, semi-distributed model, first divides the watershed into multiple subwatersheds, and then extracts the basic computation element, called the Hydrologic Response Unit (HRU). In the process of HRU generation, the spatial information of land use and soil maps within each subwatershed is lost. The SWAT model estimates the HRU topographic data based on the average slope of each subwatershed, and then use this topographic datum for all HRUs within the subwatershed. To improve the SWAT capabilities for various watershed scenarios, the Spatially Distributed-HRU (SD-HRU) pre-processor module was developed in this study to simulate site-specific topographic data. The SD-HRU was applied to the Hae-an watershed, where field slope lengths and slopes are measured for all agricultural fields. The analysis revealed that the SD-HRU pre-processor module needs to be applied in SWAT sediment simulation for accurate analysis of soil erosion and sediment behaviors. If the SD-HRU pre-processor module is not applied in SWAT runs, the other SWAT factors may be over or under estimated, resulting in errors in physical and empirical computation modules although the SWAT estimated flow and sediment values match the measured data reasonably well.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.6
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• pp.825-837
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• 2011

Seonakdong river consists of stagnant sections whose flowrate is controlled by the Daejeo and Noksan gates. As a result, there is not a minimum flow during normal times. The Daejeo and Noksan gates are located at the upstream head and the downstream end of Seonakdong river, respectively. Seonakdong river is an estuarine tributary of Nakdong river, which is a reservoir-like river used for agricultural irrigation, with the gate at the estuary of the river to prevent the intrusion of saline. Since the construction of the water gates, the water quality of the river has become degraded. This could also be due to the internal loading of pollutants, especially nutrients, from the sediments of the river because of the elongated detention time by the water gates. This study was thus conducted for the purpose of evaluating the current hydrologic-cycle system and providing measures for the rehabilitation of the hydrologic cycle. In this research, the daily outflow in Seonakdong River was simulated using the SWAT and SWMM models, and the water quality concentration including BOD, SS, TN, and TP were analyzed. The possibility of the application of SWAT-SWMM hybrid simulation was determined through the verification of both models. The error analysis shows that the results of both SWAT and SWAT-SWMM simulations make good agreements with those of field observations. For the single simulation results of SWAT, $R^{2}$ and NSE are 0.758, 0.511, respectively. For the hybrid simulation results of SWAT-SWMM, those are 0.880, 0.452, which means that the hybrid simulation can give more accurate results for the watershed where both the agricultural and urban areas exist.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.505-515
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• 2000

THe purpose of this study is to forecast of runoff hydrographs according to rainfall event in a stream. The neural network theory as a hydrologic blackbox model is used to solve hydrological problems. The Back-Propagation(BP) algorithm by the Levenberg-Marquardt(LM) techniques and Radial Basis Function(RBF) network in Neural Network(NN) models are used. Runoff hydrograph is forecasted in Bocheongstream basin which is a IHP the representative basin. The possibility of a simulation for runoff hydrographs about unlearned stations is considered. The results show that NN models are performed to effective learning for rainfall-runoff process of hydrologic system which involves a complexity and nonliner relationships. The RBF networks consist of 2 learning steps. The first step is an unsupervised learning in hidden layer and the next step is a supervised learning in output layer. Therefore, the RBF networks could provide rather time saved in the learning step than the BP algorithm. The peak discharge both BP algorithm and RBF network model in the estimation of an unlearned are a is trended to observed values.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.1-8
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• 2019

In this study, the multi-objective optimization method is attemped to optimize the hydrological model to estimate the runoff through two hydrological processes. HL-RDHM, a distributed hydrological model that can simultaneously estimate the amount of snowfall and runoff, was used as the distributed hydrological model. The Durango River basin in Colorado, USA, was selected as the watershed. MOSCEM was used as a multi-objective optimization method and parameter calibration and hydrologic model optimization were tried by selecting 5 parameters related to snow melting and 13 parameters related to runoff. Data from 2004 to 2005 were used to optimize the model and verified using data from 2001 to 2004. By optimizing both the amount of snow and the amount of runoff, the RMSE error can be reduced from 7% to 40% of the simulation value based on the initial solution at three SNOTEL points based on the RMSE. The USGS observation point of the outflow is improved about 40%.

• Journal of Korean Society on Water Environment
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• v.34 no.6
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• pp.569-578
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• 2018

To minimize the negative alterations in hydrologic and water quality environment in urban areas due to urbanization, Low Impact Development (LID) techniques are actively applied. In Korea, LID facilities are classified as Non-point Pollution Reduction Facilities (NPRFs), and therefore they are evaluated using the performance evaluation method for NPRFs. However, while LID facilities are generally installed in small, distributed configuration and mainly work with the infiltration process, the existing NPRFs are installed on a large scale and mainly work with the reservoir process. Therefore, some limitations are expected in assessing both facilities using the same method as they differ in properties. To solve these problems, in this study, a new method for performance evaluation was proposed with focus on bio-retention LID facilities. EPA SWMM was used to reproduce the hydrologic and water quality phenomena in study area, and SWMM-LID module used to simulate TP interception performance by installing a bio-retention cell under various conditions through long-term simulations. Finally, an empirical formula for Load Capture Ratio (LCR) was derived based on storm water interception ratio in the same form as the existing method. Using the existing formula in estimating the LCR is likely to overestimate the performance of interception for non-point pollutants in the extremely low design capacity, and also underestimate it in the moderate and high design capacity.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.85-96
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• 2021

Irrigation return flow is defined as the excess of irrigation water that is not evapotranspirated by direct surface drainage, and which returns to an aquifer. It is important to quantitatively estimate the irrigation return flow of the water cycle in an agricultural watershed. However, the previous studies on irrigation return flow rates are limitations in quantifying the return flow rate by region. Therefore, simulating irrigation return flow by accounting for various water loss rates derived from agricultural practices is necessary while the hydrologic and hydraulic modeling of cultivated canal-irrigated watersheds. In this study, the irrigation return flow rate of agricultural water, especially for the entire agricultural watershed, was estimated using the SWMM (Storm Water Management Model) module from 2010 to 2019 for the Madun reservoir located in Anseong, Gyeonggi-do. The results of SWMM simulation and water balance analysis estimated irrigation return flow rate. The estimated average annual irrigation return flow ratio during the period from 2010 to 2019 was approximately 55.3% of the annual irrigation amounts of which 35.9% was rapid return flow and 19.4% was delayed return flow. Based on these results, the hydrologic and hydraulic modeling approach can provide a valuable approach for estimating the irrigation return flow under different hydrological and water management conditions.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.499-514
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• 2011

An underlying assumption of traditional hydrologic frequency analysis is that climate, and hence the frequency of hydrologic events, is stationary, or unchanging over time. Under stationary conditions, the distribution of the variable of interest is invariant to temporal translation. Water resources infrastructure planning and design, such as dams, levees, canals, bridges, and culverts, relies on an understanding of past conditions and projection of future conditions. But, Water managers have always known our world is inherently non-stationary, and they routinely deal with this in management and planning. The aim of this paper is to give a brief introduction to non-stationary extreme value analysis methods. In this paper, a non-stationary hydrologic frequency analysis approach is introduced in order to determine probability rainfall consider changing climate. The non-stationary statistical approach is based on the conditional Generalized Extreme Value(GEV) distribution and Maximum Likelihood parameter estimation. This method are applied to the annual maximum 24 hours-rainfall. The results show that the non-stationary GEV approach is suitable for determining probability rainfall for changing climate, sucha sa trend, Moreover, Non-stationary frequency analyzed using SOI(Southern Oscillation Index) of ENSO(El Nino Southern Oscillation).

• Journal of Environmental Science International
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• v.15 no.8
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• pp.775-784
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• 2006

In this study, CN value was estimated by using detailed soil map and land cover characteristic against upper basin of Kumho watermark located on the upper basin of Kumho river and the hydrologic morphological characteristic factors were extracted from the basin by using the DEM document. Also the runoff analysis was conducted by the WMS model in order to study how the assumed CN value affects the runoff characteristic. First of all, as a result of studying the soil type in this study area, mostly D type soil was Identified by the application of the 1987 classification criteria. However, by that in 1995, B type soil and C type soil were distributed more widely in that area. When CN value was classified by the 1995 classification criteria, it was estimated lower than in 1987, as a result of comparing the estimated CNs by those standars. Also it was assumed that CN value was underestimated when the plan for Geum-ho river maintenance was drawn up. As a result of the analysis of runoff characteristic, the pattern of generation of the classification criteria of soil groups appeared to be similar, but in the case of the application of the classification criteria in 1995, the peak rate of runoff was found to be smaller on the whole than in the case of the application of the classification criteria in 1987. Also when the statistical data such as the prediction errors, the mean squared errors, the coefficient of determination and other data emerging from the analysis, was looked over in total, it seemed appropriate to apply the 1995 classification criteria when hydrological soil classification group was applied. As the result of this study, however, the difference of the result of the statistical dat was somewhat small. In future study, it is necessary to follow up evidence about soil application On many more watersheds and in heavy rain.