• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1B
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• pp.11-22
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• 2009

Long-term rainfall-runoff modeling is a key element in the Earth's hydrological cycle, and associated with many different aspects such as dam design, drought management, river management flow, reservoir management for water supply, water right permission or coordinate, water quality prediction. In this regard, hydrologists have used the hydrologic models for design criteria, water resources assessment, planning and management as a main tool. Most of rainfall-runoff studies, however, were not carefully performed in terms of considering reservoir effects. In particular, the downstream where is severely affected by reservoir was poorly dealt in modeling rainfall-runoff process. Moreover, the effects can considerably affect overall the rainfallrunoff process. An objective of this study, thus, is to evaluate the impact of reservoir operation on rainfall-runoff process. The proposed approach is applied to Anseong watershed, where is in a mixed rural/urban setting of the area and in Korea, and has been experienced by flood damage due to heavy rainfall. It has been greatly paid attention to the agricultural reservoirs in terms of flood protection in Korea. To further investigate the reservoir effects, a comprehensive assessment for the results are discussed. Results of simulations that included reservoir in the model showed the effect of storage appeared in spring and autumn when rainfall was not concentrated. In periods of heavy rainfall, however, downstream runoff increased in simulations that do not consider reservoir factor. Flow duration curve showed that changes in streamflow depending upon the presence or absence of reservoir factor were particularly noticeable in ninety-five day flow and low flow.

• Korean Journal of Ecology and Environment
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• v.33 no.2 s.90
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• pp.109-118
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• 2000

The dynamics of inorganic nutrients and phytoplankton population were examined at eight stations of Shihwa Reservoir, which situated near the cities newly constructed and the industrial complex of West-sea in Korea, from January to December 1999. Among environmental factors, average concentration of $NH_4$, SRP and SRSi were $522.7\;{\mu}g\;N/l$, $9.8\;{\mu}g\;N/l$ and $0.26\;{\mu}g\;Si/l$, respectively. Water quality was extremely deteriorated by a great amount of pollutants load into inner reservoir after the event of rainfall. Nutrients concentration was suddenly decreased toward the lower part. While $NO_3$ concentration did not much varied among stations, but it was relatively high in winter season. Chlorophyll-a concentration was high at the upper part of the reservoir, with average of $37.2\;{\mu}/l$, and closely related to the fluctuation of $NH_4$, SRP and SRSi concentrations. The phytoplankton development in the water column was dominated by diatom (autumn), prasinoid (winter) and dinoflagellate (summer). Dominant phytoplankton were composed to Skeletonema costatum of diatom, Prorocentrum minimum of dinoflagellate, Chroomonas spp. of cryptomonad, Eutreptiella gymnastica of euglenoid and Pyramimonas spp. of prasinoid. The large bloom of phytoplankton at the upper zone of the Shihwa Reservoir after inflow of a seawater were consistently observed. In consequence, water quality management of the inlet stream was assessed to be very important and urgent.

• Journal of Korea Water Resources Association
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• v.45 no.10
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• pp.969-981
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• 2012

In this study, we analysed hydrologic variability in quantity and onset of annual maximum flow and low flow by impacts of the different phases of ENSO (El Ni$\tilde{n}$o Southern Oscillation) over the Han River Basin. The results show that annual maximum flow has increased statistically significant about 48.3% of all over the watershed. The onset of annual maximum flow was delayed in the west of the Han River basins and in the east of the basins was likely to be rapid onset. Also, this study shows that 7-day low flow was deceased statistically significant about 26.0% of the total area in the Han River Basin, and onset of 7-day low flow tends to be faster in the upper-middle basins of the Han River. The onset of annual maximum flow shows similar pattern during the CT (Cold tongue)/WP (Warm-pool) El Ni$\tilde{n}$o years, but annual maximum flow appeared less in 89.0% of all basins during the CT El Ni$\tilde{n}$o years. In addition, the onset of 7-day low flow tended to be faster about 17 days on average during the WP El Ni$\tilde{n}$o years, and 72.7% of the basins show significant increase during the CT El Ni$\tilde{n}$o years. Consequently, it was found that the different phases of CT/WP El Ni$\tilde{n}$o have effects on sensitivity to variability in quantity and onset of water resources over the Han River Basin. We expect that the present diagnostic study on hydrological variability during different phases of ENSO will provide useful information for long-term prediction and water resources management.