• Journal of Korean Society of Forest Science
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• v.77 no.3
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• pp.269-275
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• 1988

To investigate the cause of overflow in the torrential stream, the estimated peak flow of run-off and the maximum tarring capacity of the stream were measured at the upstream of Samsung-cheon located in Kwanak Aboretum during July, 1987. The results obtained from this study could be summarized as follows : 1. The surveyed catchment area was 477ha, which was 116 of the designed area (410ha) by the plan. 2. The maximum rainfall intensity measured was 99.5mm/hr and was almost same as the designed intensity(100mm/hr). 3. The surveyed run-off coefficient was 0.672 that was about twice as much as designed one(0.35). 4. The surveyed peak flow of run-off was $88.59m^3/sec$, 222% as large the designed one($39.9m^3/sec$). 5. The designed cross-sectional area of the stream was $17.25m^2$, which was 68% of the designed one$25.43m^2$. 6. The surveyed hydraulic mean radius was 0.94m, which was shorter than the designed one(1.28m). 7. The surveyed mean stream-bed gradient(0.998%) was almost the same as the designed one(1.00%). 8 The surveyed maximum velocity of flow passing through the stream was 2.87m/sec, 78.0 of the designed one(3.68m/sec). 9 The surveyed run-off capacity of the stream was $49.51m^3/sec$, 53% of the designed one ($93.5m^3/sec$).

• Journal of Korean Society of Environmental Engineers
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• v.36 no.8
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• pp.561-569
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• 2014

This study was carried out to get the characteristics of anaerobic digestion for chemical/biological sludge produced from municipal sewage treatment plant for phosphorus. Anaerobic mesophilic batch tests showed that the ultimate biodegradability of waste activated sludge showed 31%, PACl sludge 24%, Alum sludge 26%, respectively. At the S/I 1.0, 75% of total biodegradable volatile solids (TBVS) of waste activated sludge was degraded with an initial rapid decay coefficient, k1 of $0.1129day^{-1}$ and 74% of TBVS of PACl sludge with k1 of $0.0998day^{-1}$, and 76% of TBVS of Alum sludge with k1 of $0.1091day^{-1}$ for 20 days. During the operation of SCFMRs, the 3 reactor (Control, PACl, Alum) pH maintained 6.7~7.0 and the reactor alkalinity maintained 1,800~ 2,200 mg/L as $CaCO_3$. The average biogas production rates of SCFMRs fed with PACl sludge and Alum sludge were 0.089 v/v-d and 0.091 v/v-d, respectively, which was 27~28% lower than that of the control (0.124 v/v-d) at an HRT (hydraulic retention times) of 20 days. And the methane content during the operation ranged 70~76% in 3 reactor. The average TVS removal efficiency of SCFMRs fed with PACl sludge and Alum sludge were 19.6% and 19.9%, respectively, at an HRT of 20 days, which showed 4% lower than that of the control (23.8%). The average BVS removal efficiency of SCFMRs fed with PACl sludge and Alum sludge were 25.8% and 26.9%, respectively, at an HRT of 20 days, which was 8~9% lower than that of the control (34.5%).

• 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.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.435-445
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• 2023

A delta is a depositional landform that is formed when sediment transported by a river is deposited in a relatively low-energy environment, such as a lake, sea, or a main channel. Among these, a delta formed at the confluence of rivers has a great importance in river management and research because it has a significant impact on the hydraulic and sedimentological characteristics of the river. Recently, the equilibrium state of the confluence area has been disrupted by large-scale dredging and construction of levees in the Nakdong River. However, due to the natural recovery of the river, the confluence area is returning to its pre-dredging natural state through ongoing sedimentation. The time-series data show that the confluence delta has been steadily growing since the dredging, but once it reaches a certain size, it repeats growth and retreat, and the overall size does not change significantly. In this study, we developed a model to explain the sedimentation-erosion processes in the confluence area based on the assumption that the confluence delta reaches a dynamic equilibrium. The model is based on two fundamental principles: sedimentation due to supply from the tributary and erosion due to the main channel. The erosion coefficient that represents the Nakdong River confluence areas, was obtained using data from the tributaries of the Nakdong River. Sensitivity analyses were conducted using the developed model to understand how the confluence delta responds to changes in the sediment and water discharges of the tributary and the main channel, respectively. We then used annual average discharge of the Nakdong River's tributaries to predict the dynamic equilibrium positions of the confluence deltas. Finally, we conducted a simulation experiment on the development of the Gamcheon-Nakdong River delta using recorded daily discharge. The results showed that even though it is a simple model, it accurately predicted the dynamic equilibrium positions of the confluence deltas in the Nakdong River, including the areas where the delta had not formed, and those where the delta had already formed and predicted the trend of the response of the Gamcheon-Nakdong River delta. However, the actual retreat in the Gamcheon-Nakdong River delta was not captured fully due to errors and limitations in the simplification process. The insights through this study provide basic information on the sediment supply of the Nakdong River through the confluence areas, which can be implemented as a basic model for river maintenance and management.