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This study used the United States Environmental Protection Agency's (US EPA) stormwater management model (SWMM) to quantify nonpoint source (NPS) pollutant load from the combined sewer in the Nakdong River basin, Korea. In the simulation using SWMM, the river basin consisted of 39 subwatersheds with 48 sanitation districts. For each sanitation district, combined sewer overflow (CSO) and bypass flow of the sewer treatment plant (STP) were estimated as NPS discharge. In the simulation, it assumed that a STP can receive up to three times the design peak flow rate of the STP and bypass the remainder of the intercepted stormwater during a storm event. The model was calibrated and validated with the observed water quantity and quality data in 2006 and 2007, respectively. As a result of the simulation, the estimated average load of biochemical oxygen demand (BOD) was 409.5 kg/ha/yr and 62.8 kg/ha/yr for the sanitation coverage (proportion of the area covered by the sewer system in a sanitation district) of above 40% and below 10%. The results showed that different unit loads of NPS can be estimated from each sanitation district depending on the degree of urbanization. [ABSTRACT FROM AUTHOR]

As a river basin pollution runoff quality indicator, delivery ratio is an important factor in river basin management but the lack of empirical data and absence of practical application method as well as increase in non-point pollution source make assessment of delivery ratio for flow duration difficult. Therefore, this research puts into consideration the river basin of Nakdong river's water system with water quality and flow data secured by real time auto monitoring that takes place at the area of subject of research. This survey on the spot and water basin model law provides practical assessment of individual case of flow duration and river basin delivery ratio and is applied on the area of research. By careful consideration of land use types in Nakdong river's water system, mountainous, agricultural land, and urban property area is governed and an adequate area that represents the characteristics of river basin delivery ratio is chosen by use of real time monitoring based on land use. Through the SWAT results obtained by monitoring results, flow duration curve (FDC) and loads duration curve (LDC) of three property areas over the three year between 2000-2007 have been written along with a fi nal delivery rate duration curve (DRDC). Monitoring data by land use and FDC, LDC, DRDC as basis, polluted substance runoff characteristics and polluted substance delivery characteristics have been analyzed according to regional land use for overall analysis of individual river basin runoff and delivery characteristics. Urban property area has shown high delivery and agricultural property area has shown low delivery. Legibility in the statement of different delivery ratio has been obtained by checking the difference in delivery ratio that proved each river basin to have differing delivery ratio according to its topographic characteristics. [ABSTRACT FROM AUTHOR]

In recent, South Korea has faced on water quality management problems in reservoir and river because of increasing water temperature (Tw) and rainfall frequency caused by climate change. For these reasons, South Korea has set up automatic water quality monitoring system for preventing early algae blooms in five representative watershed. Also, Government makes a greater effort for preparing remedy with numerical models which handle water quality accidents quality in advance by predicting variation of water quality factors on account of change of weather conditions and source of pollutants in the future. Many countries have conducted various studies on forecasting water quality by artificial neural network which has a number of advantages, as opposed to the traditional models based on methods like data driven self-adaptive methods, generalization through learning the data presented, universal functional approximators, and nonlinear for forecasting. Daecheong reservoir located in Geum river has suitable environment for algae blooms because it has lots of contaminants that are flowed by rainfall in Daejeon and Chungcheong area. This study selected Daecheong reservoir in the Geum river and used the data of the automatic water quality observation system. By using back propagation algorithm (BPNN) of feed forward neural networks, a model has been built to forecast the algae blooms over short periods (1, 3 and 7 d). In this model, input parameters considered the hydrologic and water quality factors as following: inflow, outflow, average areal precipitation, air temperature (Ta), Tw, dissolved oxygen (DO), total organic carbon (TOC), pH, chlorophyll-a (chl-a), total nitrogen (TN), and total phosphorous (TP) in Daecheong reservoir. However, the chl-a data of automatic water quality observation system has some missing data caused by defect and maintenance in the system. Through carrying out correlation analysis, interpolation has been implemented by neural network with BPNN. Correlation analysis has been implemented to analyze lag time and components that sensitively responded to chl-a by referring the interpolated data and water quality and hydrologic factors in all. Based on the results of the data, after selecting input parameters for algae bloom prediction model, each case has been verified along with making various models. As a result of this research, the short term algae bloom prediction models showed minor errors in the prediction of the 1 d and the 3 d. Components of water quality such as Tw, pH, and TOC showed the closest correlation with chl-a and the models have been built with them. Therefore, the models will be very effective to control the water quality of Daecheong reservoir in South Korea by predicting a day to seven days. [ABSTRACT FROM AUTHOR]

Water quality control is affected by several factors such as climate change or the creation of an ecological park for human convenience. The Four-River restoration project of Korea is a government enterprise to solve a variety of problems, such as preventing floods, securing water resources, water quality management, and encouraging re-creation of land. It is evidence that technical developments and the concern of the government have sharply increased for water quality management. In particular, the phenomenon of eutrophication can cause various difficulties in drinking water treatment and water use. Accurate and reliable algal bloom forecasting models will prove very useful in ensuring sustainable water supply and proper water management in the near future. In this paper, a new method based on wavelet transforms and artificial neural networks was adopted for chlorophyll-a concentration forecasting 1, 3 and 7 days ahead. First, 12 models for forecasting chlorophyll-a concentration by combining water quality and hydrological factors from different models as input data were established by using an original ANN with a back-propagation algorithm. The best model, as evaluated by its performance functions, was selected and applied to the new method as a coupled wavelet analysis-artificial neural network (WA-ANN) to forecast chlorophyll-a concentration for 1, 3 and 7 days. Finally, the results of WA-ANN in the study were compared to those of a regular ANN with a back-propagation algorithm. The results showed that WA-ANN models constitute a promising new method for short-term chlorophyll-a concentration forecasting in large lakes. [ABSTRACT FROM AUTHOR]

Various techniques are being developed and applied for the management of non-point pollutants. Especially, there has been an increasing interest in the techniques which work for both the restoration of hydrologic cycle and management of non-point pollutants. When the need for the management of non-point pollutants first arose, structural concept of BMP was developed. Recent years saw the expansion of management plans including precautionary land use techniques such as low impact development (LID). LID is usually applied to new development areas but lately is in extensive use with existing city areas. To analyze the effect of LID application on an industrial complex by using Strom Water Management Model (SWMM), a point with clear division of the soil pipe and rainwater pipe sections was selected for monitoring non-point pollutant runoff and analyzing the characteristics of the runoff in this study. Furthermore, to construct a SWMM-LID model of a small-scale catchment, RUNOFF blocks were subdivided into sections and six event data with satisfactory flow pattern were selected from 24 monitoring data collected over three years (2009-2011) to be calibrated, two more event data were used for verification to complete the construction of the model. LID key technologies were applied as four CASEs: existing condition of the city (CASE 1), application of rain barrel (CASE 2), rain barrel and tree filter box (CASE 3), rain barrel, tree filter box, and porous pavement (CASE 4) for short-term and long-term (seasonal) simulation analyses. [ABSTRACT FROM AUTHOR]