• Journal of Korean Society of Environmental Engineers
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• v.33 no.12
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• pp.874-885
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• 2011

In this study, the urban $CO_2$ emission based on energy consumption (Coal, Petroleum, Electricity, and City Gas) in 16 provincial and metropolitan city governments in South Korea was evaluated. For calculation of the urban $CO_2$ emission, direct and indirect emissions were considered. Direct emissions refer to generation of greenhouse gas (GHG) on-site from the energy consumption. Indirect emissions refer to the use of resources or goods that discharge GHG emissions during energy production. The total GHG emission was 497,083 thousand ton $CO_2eq.$ in 2007. In the indirect GHG emission, about 240,388 thousand ton $CO_2eq.$ was occurred, as 48% of total GHG emission. About 256,694 thousand ton $CO_2eq.$ (52% of total GHG emissions) was produced in the direct GHG emission. This amount shows 13% difference with 439,698 thousand ton $CO_2eq.$ which is total national GHG emission data using current calculation method. Local metropolitan governments have to try to get accuracy and reliability for quantifying their GHG emission. Therefore, it is necessary to develop and use Korean emission factors than using the IPCC (Intergovernmental Panel on Climate Change) emission factors. The method considering indirect and direct GHG emission, which is suggested in this study, should be considered and compared with previous studies.

• 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.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.5
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• pp.103-115
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• 2016

This study aims to analyze the properties of cold air production and its flow of Nakdong-Jeongmaek(mountain ranges), and to suggest management strategies for Nakdong-Jeongmaek in order to enhance the green air conditioning functions of Jeongmaek. For this purpose, three study sites including Gudeoksan Mountain and the vicinity in Busan, Goheonsan Mountain and the vicinity in Ulsan, and Unjusan Mountain and the vicinity in Pohang were selected. The results found that cold air flow and its height of the three study sites were analyzed based on topographic properties and land use. Management strategies for preserving and enhancing their temperature reduction functions were suggested. The cold air produced in the vicinity of Gudeoksan was not fully developed and spread because of the high-density development at the border of Jeongmaek. Since high pressures of development are expected at the border, high conservation policies are required. In the vicinity of Goheonsan, where the agricultural complex and industrial park are located, cold air flows well throughout the entire study site thanks to fully developed cold air in the wide, flat valley. Hence, plans to maintain the current cold air flow are required, and conservation plans to mitigate future developments are also needed in the flat valley. The cold air in Unjusan and the vicinity with its complex and narrow mountain valleys gradually develops into valley bottoms. In order to take advantage of the terrain, the valley near the cold air production areas are preserved. In particular, special plans are required to prevent damage to the cold air layer near Youngcheonho Lake, where the highest height of cold air was recorded due to the closed and lower terrain feature. This study could support the establishment of systematic management plans of Nakdong-Jeongmaek to preserve and enhance its green air conditioning functions.