• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.873-884
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• 2013

In Korea, the rainfall is concentrated in summer under the influence of monsoon climate. Thus, even a small climate change can be significant problems in water resources. As a result, a lot of attention has been focused on climate changes and a number of researches have been conducted in a manner commensurate with the attention to the climate change. This study is intended to forecast the changes in the flow and water quality of the Nam river resulting from the future climate changes in the Nam river basin using a watershed and water quality model. An SWAT model, as a watershed hydrologic model, was established after estimating a climate scenario using an artificial neural network method, and the established model was verified and adjusted using date from the Ministry of Environment to evaluate the applicability of the model. As a consequence, $R^2$ showed more than 0.7 in the simulation test, which satisfies the minimum required level. Results from the SWAT model and the future Namgang dam discharge calculated by HEC-ResSIM is used as input date for QUALKO. The results showed a huge variation in BOD depending on the annual flow of the river, which recorded a maximum difference of 2 mg/L between a rainy season and a dry season. It can be deduced that because rainfall and the runoff of a basin significantly account for the water quality of a river, higher water concentrations are recorded in a dry season in which the flow is not as much as that in a rainy season. It also can be said that water should be reserved in advance to secure water in the Nam river downstream for a dry season and be controlled in an effective and efficient manner to provide better water quality.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.91-101
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• 2023

In this study, we estimated the concentration of chlorophyll-a (Chl-a) using hyperspectral water surface reflectance in an inland weir (Baekjae weir) and estuarine reservoir (Namyang Reservoir) for monitoring the occurrence of algae in freshwater in South Korea. The hyperspectral reflectance was measured by aircraft in Baekjae Weir (BJW) from 2016 to 2017, and a drone in Namyang Reservoir (NYR) from 2020 to 2021. The 30 reflectance bands (BJW: 400-530, 620-680, 710-730, 760-790 nm, NYR: 400-430, 655-680, 740-800 nm) that were highly related to Chl-a concentration were selected using permutation importance. Artificial neural network based Chl-a estimation model was developed using the selected reflectance in both water bodies. And the performance of the model was evaluated with the coefficient of determination (R²), the root mean square error (RMSE), and the mean absolute error (MAE). The performance evaluation results of the Chl-a estimation model for each watershed was R²: 0.63, 0.82, RMSE: 9.67, 6.99, and MAE: 11.25, 8.48, respectively. The developed Chl-a model of this study may be used as foundation tool for the optimal management of freshwater algal blooms in the future.