• 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.

• Korean Journal of Environmental Agriculture
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• v.26 no.2
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• pp.99-106
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• 2007

This study was focused on understanding the agricultural non-point sources pollution in 72 rural catchments of Nakdong river watershed from 2001 to 2005 every two year. Also. Pearson correlations between water quality and basin characteristic were computed. Water quality of this study watershed was better in 2003 than any other period. The water quality of upstream was recorded from 0.040 to 0.510 dS/m in EC, from 3.55 to 22.60 mg/L in DO, from 0.32 to 16.64 mg/L in T-N, from 0.00 to 12.21 mg/L in $NO_3-N$, from 0.000 to 0.860 mg/L in T-P, and from 0.000 to 0.640 mg/L in $PO_4-P$. A the downstream, EC was measured from 0.030 to 0.520 dS/m, DO from 4.13 to 18.36 mg/L, T-N from 0.38 to 26.88 mg/L, $NO_3-N$ from 0.10 to 20.12 mg/L, T-P from 0.002 to 0.820 mg/L, $PO_4-P$ from 0.002 to 0.690 mg/L. But there was no difference between upstream and downstream for the water quality. Based on the correlation analysis between water quality and land use, correlation between BOD and residential was the highest positive correlation of 0.541 (p<0.01), and correlation between $PO_4-P$ and forest was the highest negative correlation of -0.451 (p<0.01). Also, T-N, $NO_3-N$, and pH were not correlated with all basin characteristics and basin was not correlated with all water quality parameter. According to the correlation residential was causative of growing worst for water quality, and forest was causative of improving for water quality.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.325-340
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• 2008

This study employs Bayesian multiple regression analysis using the ordinary least squares method for regional low flow frequency analysis. The parameter estimates using the Bayesian multiple regression analysis were compared to conventional analysis using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian analysis at each return period are not significantly different. However, the difference between upper and lower limits is remarkably reduced using the Bayesian multiple regression. Therefore, from the point of view of uncertainty analysis, Bayesian multiple regression analysis is more attractive than the conventional method based on a t-distribution because the low flow sample size at the site of interest is typically insufficient to perform low flow frequency analysis. Also, we performed low flow prediction, including confidence interval, at two ungauged catchments in the Nakdong River basin using the developed Bayesian multiple regression model. The Bayesian prediction proves effective to infer the low flow characteristic at the ungauged catchment.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.955-968
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• 2021

A water resource plan is routinely based on a natural flow and can be estimated using observed streamflow data or a long-term continuous rainfall-runoff model. However, the watershed with the natural flow is very limited to the upstream area of the dam. In particular, for the ungauged watershed, a rainfall-runoff model is established for the gauged watershed, and the model is then applied to the ungauged watershed by transferring the associated parameters. In this study, the GR4J rainfall-runoff model is mainly used to regionalize the parameters that are estimated from the 14 dam watershed via an optimization process. In terms of optimizing the parameters, the Bayesian approach was applied to consider the uncertainty of parameters quantitatively, and a number of parameter samples obtained from the posterior distribution were used for the regionalization. Here, the relationship between the estimated parameters and the topographical factors was first identified, and the dependencies between them are effectively modeled by a Copula function approach to obtain the regionalized parameters. The predicted streamflow with the use of regionalized parameters showed a good agreement with that of the observed with a correlation of about 0.8. It was found that the proposed regionalized framework is able to effectively simulate streamflow for the ungauged watersheds by the use of the regionalized parameters, along with the associated uncertainty, informed by the basin characteristics.

• Korean Journal of Remote Sensing
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• v.14 no.2
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• pp.175-195
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• 1998

Automatic lineament extraction algorithms had been developed by various researches for geological purpose using remotely sensed data. However, most of them are designed for a certain topographic model, for instance rugged mountainous region or flat basin. Most of common topographic characteristic in Korea is a mountainous region along with alluvial plain, and consequently it is difficult to apply previous algorithms directly to this area. A new algorithm of automatic lineament extraction from remotely sensed images is developed in this study specifically for geological applications. An algorithm, named as DSTA(Dynamic Segment Tracing Algorithm), is developed to produce binary image composed of linear component and non-linear component. The proposed algorithm effectively reduces the look direction bias associated with sun's azimuth angle and the noise in the low contrast region by utilizing a dynamic sub window. This algorithm can successfully accomodate lineaments in the alluvial plain as well as mountainous region. Two additional algorithms for estimating the individual lineament vector, named as ALEHHT(Automatic Lineament Extraction by Hierarchical Hough Transform) and ALEGHT(Automatic Lineament Extraction by Generalized Hough Transform) which are merging operation steps through the Hierarchical Hough transform and Generalized Hough transform respectively, are also developed to generate geological lineaments. The merging operation proposed in this study is consisted of three parameters: the angle between two lines($\delta$$\beta$), the perpendicular distance($(d_ij)$), and the distance between midpoints of lines(dn). The test result of the developed algorithm using Landsat TM image demonstrates that lineaments in alluvial plain as well as in rugged mountain is extremely well extracted. Even the lineaments parallel to sun's azimuth angle are also well detected by this approach. Further study is, however, required to accommodate the effect of quantization interval(droh) parameter in ALEGHT for optimization.