• Journal of Wetlands Research
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• v.17 no.2
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• pp.193-202
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• 2015

In this study, a long term monitering of nonpoint source pollution runoff is conducted at the area of transportation related and EMCs(Event Mean Concentrations) in terms of water quality items, such as BOD, $COD_{Mn}$, SS, T-N and T-P are determined for each not only runoff event and but also observation site. On the other hands, SWMM(Storm Water Management Model) model is constructed using the data collected in the transportation areas selected. Model calibration and verification of SWMM is carried out based on the data collected. And simulated EMCs was compared with observed EMCs by monitoring and prior studies. SWMM applicability estimation was Using the compared result. The results of simulation showed that BOD 5.787 ~ 14.475 mg/L, $COD_{Mn}$ 12.946 ~ 59.611 mg/L, SS 13.742 ~ 46.208 mg/L, T-N 2.037 ~ 5.213 mg/L, T-P 0.117 ~ 0.415 mg/L. And a differential between simulated EMCs and observed EMCs is too low so comparing result show high fit(BOD 4.27 %, $COD_{Mn}$ 4.87%, SS 2.31%, T-N 5.78%, T-P 14.45%). A results of compared with the prior studies, BOD and T-P are included range of prior studies, $COD_{Mn}$ and SS are lower than range of prior studies, T-N is higher than range of prior studies. Differential between simulated EMCs and prior studies EMCs was showing for survey seasonal and changing land-use, so from now on, EMCs of using the internal representatives value will be calculated by more monitoring toward various precipitation events.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of Korea Water Resources Association
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• v.42 no.8
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• pp.643-657
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• 2009

The major reason to construct large dams is to store surplus water during rainy seasons and utilize it for water supply in dry seasons. Reservoir storage has to meet a pre-defined target to satisfy water demands and cope with a dry season when the availability of water resources are limited temporally as well as spatially. In this study, a Hedging rule that reduces total reservoir outflow as drought starts is applied to alleviate severe water shortages. Five stages for reducing outflow based on the current reservoir storage are proposed as the Hedging rule. The objective function is to minimize the total discrepancies between the target and actual reservoir storage, water supply and demand, and required minimum river discharge and actual river flow. Mixed Integer Linear Programming (MILP) is used to develop a multi-reservoir operation system with the Hedging rule. The developed system is applied for the Han River basin that includes four multi-purpose dams and one water supplying reservoir. One of the fours dams is primarily for power generation. Ten-day-based runoff from subbasins and water demand in 2003 and water supply plan to water users from the reservoirs are used from "Long Term Comprehensive Plan for Water Resources in Korea" and "Practical Handbook of Dam Operation in Korea", respectively. The model was optimized by GAMS/CPLEX which is LP/MIP solver using a branch-and-cut algorithm. As results, 99.99% of municipal demand, 99.91% of agricultural demand and 100.00% of minimum river discharge were satisfied and, at the same time, dam storage compared to the storage efficiency increased 10.04% which is a real operation data in 2003.

• Journal of Korean Society on Water Environment
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• v.28 no.4
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• pp.561-573
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• 2012

The object of this study was to analyze long-term water quality gradients during 1992-2008 at six sites of Geumho River and near-by two sites of Nakdong River and their influences on fish trophic guilds and tolerance guilds along with ecological health. Water quality including biological oxygen demand (BOD), chemical oxygen demand (COD), conductivity, total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) varied largely depending on the sampling locations and seasons. Values of ambient BOD, COD, TP, and TN were greater in the downstream than in the upstream reach, and seasonal and interannual variabilities were also higher in the downstreams. This phenomenon was evident due to a dilution by the Asian monsoon rainfall during the monsoon. These outcomes indicate that point sources near the downstream are important for the chemical conditions, but also seasonal stream runoff was considered as an important factor regulating the chemical conditions. Conductivity decreased rapidly during the summer due to ionic dilution, and nutrients (N, P), BOD, COD had an inverse function of seasonal precipitation. Based on the water quality, we selected two sites (control site = $C_s$ vs. impacted site = $I_s$) for impact analysis of water chemistry on fish community and trophic/tolerant guilds. Fish guild analysis showed that species diversity was higher in the headwater stream ($C_s$) than the impacted downstream ($I_s$), and that the proportion of tolerant and omnivore species were greater in the impacted site of downstream. Comparisons of water quality between Geumho River and Nakdong River indicated that Geumho River was considered as a point source which degradated water quality to the Nakdong River. Overall, chemical water quality and fish guild analysis suggest that even if current chemical quality got better after 1996 due to continuous constructions of wastewater disposal plants near the downstreams, fish compositions of tolerant and omnivores were still dominated the community. Thus, biological restoration based on ecological health is required for the ecosystem conservation.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.8
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• pp.572-577
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• 2011

The unit load has simply been used to estimate total pollutant loading from non-point sources, however, it does not count on the variable pollutant loading according to land use characteristics and rainfall intensity. Since pollutant emission from the watershed is strongly dependent on the rainfall intensity, it is necessary to find out the relationship between pollutant loading and rainfall intensity. The objective of this study is to develop simple and easy method to compute non-point source pollution loads with consideration of rainfall intensity. Two non-point source removal facility at Gyeongan-dong (Gwangju-si) and Mohyeon-myeon (Yongin-si), Gyeonggi-do was selected to monitor total rainfall, rainfall intensity, runoff characteristics and water quality from June to November, 2010. Most of Event Mean Concentrations (EMC) of measured water quality data were higher in Gyeongan which has urban land use than in Mohyeon which has rural land use. For the case of TP (Total Phosphorus), Mohyeon has higher values by the influence of larger chemical uses such as fertilizer. The relationship between non-point source pollution load and rainfall intensity is perfectly well explained by cubic regression with 0.33~0.81 coefficients of determination($R^2$). It is expected that the pollution loading function based on the long-term monitoring would be very useful with good accuracy in computing non-point source pollution load, where a rainfall intensity is highly variable.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.973-983
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• 2014

This study focused on the selection of appropriate plant species of VFS (vegetative fiter strips) and the assessment of VFS effects for reducing NPS (non-point source) pollution from uplands. The experimental field was constructed with 1 control and 6 treated plots in the upland area of $1,500m^2$ with 5% slope which is located in Gunwi-gun, Gyeongbuk province. Six vegetation including Chufa, Common crabgrass, Barnyard grass, Turf grass, Tall fescue, Kenturky bluegrass, were applied to install VFS systems during the study period from June 2011 to Dec. 2012. The results of this study showed that 6.1~77.8% in runoff and 15.6~90.3% in TS, 49.9~96.6% in T-P, and 6.7~91.1% in T-N were reduced from the VFS treated plots. Generally high reduction effects were observed from TS, T-P, T-N, and SS, while BOD, TOC, and $NO_3^-$ showed low reductions. The best vegetation type was Turf grass showing higher reduction effects of NPS pollutions and having relatively easier maintenance efforts compared to other vegetations selected in this study. Based on these results, VFS technique found to be an effective management practice for reducing agricultural NPS pollutions in Korean upland conditions. Further study needs to be performed through various field experiments with long term monitoring in order to develop a design manual of VFS system for practical applications.

• Journal of Ecology and Environment
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• v.29 no.3
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• pp.295-303
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• 2006

Clear-cut followed by tree planting has been a conventional management practice in burned forests in Korea. Because this can considerably increase soil loss, hillslope treatments may be needed in order to improve soil stability at poorly regenerating areas. This paper reviews the effects of hillslope treatments, such as seeding, mulching and log erosion barriers, which have been applied to restore vegetation and conserve soil in burned forests in North America and Europe. Seeding has been the most common method for postfire restoration. However, the effects of seeding on vegetation cover and soil erosion are not clear and seeding with non-native species has been reported to inhibit regeneration of native vegetation. Mulching has been found to be effective at reducing soil erosion. However, this also can introduce non-native plant species and inhibit native plant regeration. Although studies on the effect of log erosion barriers are very few, it appears that log erosion barriers are effective in the period of little rainfall. Hillslope treatments for postfire restoration is not necessary for naturally regenerating areas and therefore, they should be restricted to the areas where regeneration potential is low and runoff and soil loss is considerable. Long-term monitoring is needed to assess the effectiveness of hillslope treatments on soil erosion, the introduction of non-native plant species and the inhibition of natural plant regeneration.

• Korean Journal of Ecology and Environment
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• v.35 no.1 s.97
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• pp.36-44
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• 2002

The spatial and temporal trends of water qualities in Lake Soyang was statistically analyzed in this study. The water qualities include nutrients, ionic contents and chlorophyll-a (Chl-a) measured during 1993${\sim}$2000. The rainfall intensity and runoff from the catchment appeared to play an important role in water quality trends in the lake. According to seasonal Mann-Kendall test, conductivity, TP, and Ctl-a did not show any trends of increase or decrease over the 8 year period, while TN declined slightly. It was found that the variation of TP was a function of interannual inflow and rainfall. In the analyses of spatial trend, conductivity, based on the mean by site, showed a downlake decline over the eight year period. Minimum conductivity was found in the headwaters during summer monsoon of July to August and near the dam during October. This result indicates a time-lag phenomenon that the headwater is diluted by rainwater immediately after summer monsoon rain and then the lake water near the dam is completely diluted in October. During summer period, TP and TN had an inverse relation with conductivity values. Concentrations of TP peaked during July to September in the headwaters and during September in the downlake. Also, TN increase during the summer and was more than 1.5 mg/L regardless of season and location, indicating a consistent eutrophic state. Values of Chl-a varied depending on location and season, but peaked in the midlake rather than in the headwaters during the monsoon. Regression analyses of log-transformed seasonal Chl-a against TP showed that value of $R^2$ was below 0.003 in the premonsoon and monsoon seasons but was 0.82 during the postmonsoon, indicating a greater algal response to the phosphorus during the postmonsoon. In contrast, TN had no any relations with Chl-a during all seasons.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Journal of Korea Water Resources Association
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• v.37 no.11
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• pp.897-913
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• 2004

There has been continuous efforts to manage the water resources for the required water quality criterion at river channel in Korea. However, we could not obtain the partial improvement only for the point source pollutant such as, wastewater from urban and industrial site through the water quality management. Therefore, it is strongly needed that the Best Management Practice(BMP) throughout the river basin for water quality management including non-point source pollutant loads. This problem should be resolved by recognizing the non-point source pollutant loads from upstream river basin to the outlet depends on the land use and soil type characteristic of the river basin using the computer simulation by distributed parameter model based on the detailed investigation and the application of Geographic Information System(GIS). Used in this study, Annualized Agricultural Non-Point Source Pollution (AnnAGNPS) model is a tool suitable for long term evaluation of the effects of BMPs and can be used for un gauged watershed simulation of runoff and sediment yield. Now applications of model are in progress. So we just describe the limited result. However If well have done modeling and have investigated of propriety of model, well achieve our final goal of this study.