• Journal of Environmental Impact Assessment
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• v.7 no.1
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• pp.81-91
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• 1998

The objective of this study was to use remotely sensed data, combined with in situ data, for the assessment of trophic state for Daecheong reservoir. Three Landsat TM(Thematic Mapper) imagery data were processed to portray trophic state conditions. The remotely sensed data and the measured data were obtained on 20 June 1995. Regression models have been developed between the chlorophyll-a concentration and reflectance which was converted to Landsat TM digital data. The regression model was determined based on the correlation coefficient which was higher than 0.7 and was applied to the entire study area to generate a distribution map of chlorophyll-a and trophic state. The equation, providing estimates of chlorophyll-a concentration, represented the year-to-year spatial variation of trophic zones in the reservoir. Satellite remote sensing data derived from Landsat TM had been successfully used for trophic slate mapping in Daecheong reservoir.

• Journal of Wetlands Research
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• v.7 no.1
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• pp.81-91
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• 2005

We construct the hydrology-water quality monitoring system which can watch the variations of river flow and water quality in real time. We also construct the river management system through the hydrology-water quality monitoring system that can observe water quality and its variations for preparing for the accident of river pollution. The Gyecheon basin which is located at the upstream of Heoengseong dam is selected as an experimental watershed for the system construction. The real time monitoring system for getting more correct hydrological and water quality data consists of 3-rainfall gauge station, 3-water level gauge station, and 1-water quality gauge station. We intend that the data such as rainfall, water level, velocity, flow, and water quality will be collected and we try that the data may be used for practical and other purposes.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.373-384
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• 2011

This study aims at the examination of the relative characteristics of discharge and water quality in river basins using statistical methods. For it, water quality and discharge data was collected in observed stations of Nakdong river and carried out correlation analysis, regression analysis, factor analysis and cluster analysis. And it was investigated the applicability of water quality prediction using Nearest-neighbor method. As a result, it grasped a trenditional characteristics and mutual relations between discharge an water quality data. Therefore, this results were suggested the comprehensive data and methods for a management of water quality, effective operation and policy development in Nakdong river basin.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.2
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• pp.116-125
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• 2003

Predictions of stream water quality require both estimation of pollutant loading from different sources and simulation of water quality processes in the stream. Nonpoint source pollution models are often employed for estimating pollutant loading in rural watersheds. In this study, a conjunctive application of SWAT model and WASP model was made and evaluated for its applicability based on the simulation results. Runoff and nutrient loading obtained from the SWAT model were used for generating input data for WASP model. The results showed that the simulated runoff was in good agreement with the observed data and indicated reasonable applicability. Loading for the water quality parameters predicted by WASP model also showed a reasonable agreement with the observed data. It is expected that stream water quality could be predicted by the coupled application of the two models, SWAT and WASP, in rural watersheds.

• Journal of Environmental Science International
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• v.7 no.4
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• pp.433-440
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• 1998

This study was carried out to develop the stream water quality model for the intaking station of Kongju waterworks in the Keum River system. The monthly water quality(total nitrogen and total phosphorus) with periodicity and trend were forecasted by multiplicative ARIU models and then the applicability of the models was tested based on 7 years of the historical monthly water quality data at Kongju intaking strate. The parameter estimation was made with the monthly observed data. The last one year data was used to compare the forecasted water Quality by ARU model with the observed one. The models are ARIMA(2,0,0)$\times$(0,1,1)l2 for total nitrogen, ARIMA(0,1,1)x(0,1,1)l2 for total phosphorus. The forecasting results showed a good agreement with the observed data. It is implying the applicability of multiplicative ARIMA model for forecasting monthly water quality at the Kongju site.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.255-261
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• 2004

Through spot-investigation and observation of water quality environment of Jinhae Bay, this conducts the sea movement simulation on the current water quality of Jinhae Bay, grasps the flowing characteristics of Jinhae Bay and carries out ocean pollution simulation by using the data collected. Sea movement simulation results are tested by using Ocean Investigation Bureau's data of tide and current, and are also used in the ocean pollution spreading simulation. Water quality spreading simulation is conducted by using the data of sea movement and data of observation and then the results are compared with data of spot investigation. COD condensity is calculated and the water quality characteristics of Jinhae Bay are grasped.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.38-49
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• 2012

In this study, we analyze statistical characteristics of influent water quality in Daejeon waste water treatment plant and apply a stochastic model for data generation. In the analysis, the influent water quality data from year 2003 to 2008, except for year 2006, are used. Among water quality variables, we find strong correlations between BOD and T-N; T-N and T-P; BOD and T-P; $COD_{Mn}$ and T-P; and BOD and $COD_{Mn}$. We also find that different water quality variables follow different theoretical probability distribution functions, which also depends on whether the seasonal cycle is removed. Finally, we generate the influent water quality data using the multi-season 1st Markov model (Thomas-Fiering model). With model parameters calibrated for the period 2003~2005, the generated data for 2007~2008 are well compared with observed data showing good agreement in general. BOD and T-N are underestimated by the stochastic model. This is mainly due to the statistical difference in observed data itself between two periods of 2003~2005 and 2007~2008. Therefore, we expect the stochastic model can be applied with more confidence in the case that the data follows stationary pattern.

• KCID journal
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• v.19 no.1
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• pp.19-29
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• 2012

The data of the agricultural water quality survey network was analyzed between from 1990 to 2010 in order to propose effective plans for water quality management by analyzing the characteristics of agricultural lakes and the change of water quality. The result of the analysis shows that there is a correlation between water quality and items that can be a function of water depth such as dam height, dam length, dam height/dam length ratio and active storage/surface area of lake ratio. This means that, Korean agricultural lakes, there is a correlation between water quality and water depth. Water quality of the lakes that have lower than 5m of active storage/surface area of lake ratio (effective water depth) especially tends to get worse rapidly. The Chl-a and COD concentration of Korean agricultural lakes have a tendency to increase between June and September. Therefore, we recommend first taking a water quality improvement project for the lakes preformed watershed management project, and taking a preventive short-term water quality improvement project for the unperformed lakes before June among lakes that have lower than 5m of effective water depth.

• Journal of Environmental Science International
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• v.5 no.6
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• pp.763-770
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• 1996

This study was performed to analyze the variation characteristics of writer qulity, correlation analysis of water quality data at each site and among the items of water Quality data. Water quality for analysis was monthly values of water temperature, pH, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solid, 7-N and T-P checked in Daecheong Lake from January to December, 1995. It was analyzed variation of monthly water qulity was well from February to April, water temperature and COD seemed to have high correlationships at all sites. Regression equation is COD = 0.07 Water temperature +1.23 ($R^2$: 0.7616) . Results of the correlation analysis of water quality data showed that DO had high correlationships between site 1 and site 2, BOD did site 1 and 3, COD did site 1 and 2, 55 did site 5 and 6, 7-N did 2 and 3, 7-P did site 4 and 6. Regression equations for estimate of water quality data are as follows. $DO_1$=4.46+0.59 DO, ($R^2$=0.8868), $BOD_1$ = 0, 52+0.63 BOD3 ($R^2$ = 0.6390) $COD_2$ = 0.44+0.71 $COD_1$ ($R^2$ = 0.9183), SS6 = 0.89+0.7055.($R^2$ = 0.9155) $TN_3$ = 0.151 +0.886 $TN_2$ ($R^2$ = 0.9415), $TP_4$ = 0.004+5.758 $TP_6$ ($R^2$ = 0.9669)

• Journal of Environmental Science International
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• v.8 no.3
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• pp.331-336
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• 1999

The monthly water quality data measured at 14 stations located in the Geum-River watershed were clustered into 2 to 7 clusters. Furthermore, factor analyses were conducted on Gabcheon and Yugucheon to characterize the water qualtiy, based on the information obtained from the results of culster analysis. The results of cluster analysis show that the water quality charactersitic of main stream of the Geum-River is somewhat different from that of substream of the Geum-River. Furthermore, the water quality characteristic of Gabcheon which is expected to have the most serious water quality problems in the Geum-River watershed shows the most different water quality characteristic from Yugucheon. Based ont he factor loadings in each factor, Gabcheon and Yugucheon have their own water quality characteristics. This is mainly because of composite factors such as different population density, industrial activities, and land use conditions in Gabcheon and Yugucheon subwatersheds.