• Journal of Korean Society of Coastal and Ocean Engineers
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• 제25권6호
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• pp.374-385
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• 2013

Sediment particle motions have been numerically simulated over a sinusoidal ripple. Turbulent boundary layer flows are generated by Large Eddy Simulation, and the sediment particle motions are simulated using Lagrangian particle tracking method. Two unsteady flow conditions are used in the experiment by employing two different wave amplitudes while keeping other conditions such as wave period same. As expected, the amount of suspended sediment particles is clearly dependent on the wave amplitude as it is increasing with increasing flow intensity. However, it is also observed that the pattern of suspension may be different as well due to the only different condition caused by wave amplitude. Specially, the time of maximum sediment suspension within the wave period is not coincident between the two cases because sediment suspension is strongly affected by the existence of turbulent eddies that are formed at different times over the ripple between the two cases as well. The role of these turbulent eddies on sediment suspension is important as it is also confirmed in previous researches. However, it is also found the time of these eddies' formation may also dependent on the wave amplitude over rippled beds. Therefore, it has been proved that various flow as well as geometric conditions under waves has to be considered in order to have better understanding on the sediment suspension process over ripples. In addition, it is found that high turbulent energy and strong upward flow velocities occur during the time of eddy formation, which also supports high suspension rate at these time steps. The results indicate that the relationship between the structure of flows and bedforms has to be carefully examined in studying sediment suspension at coastal regions.

• Korean Journal of Ecology and Environment
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• 제47권3호
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• pp.167-175
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• 2014

This study was conducted to quantify a carbon budget of major vegetation types established in the campus of the National Institute of Ecology (NIE). Carbon budget was measured for Pinus thunbergii and Castanea crenata stands as the existing vegetation. Net Primary Productivity (NPP) was determined by applying allometric method and soil respiration was measured by EGM-4. Heterotrophic respiration was calculated as 55% of total respiration based on the existing results. Net Ecosystem Production (NEP) was determined by the difference between NPP and heterotrophic respiration (HR). NPPs of P. thunbergii and C. crenata stands were shown in $4.9ton\;C\;ha^{-1}yr^{-1}$ and $5.3ton\;C\;ha^{-1}yr^{-1}$, respectively. Heterotrophic respirations of P. thunbergii and C. crenata stands were shown in $2.4ton\;C\;ha^{-1}yr^{-1}$ and $3.5ton\;C\;ha^{-1}yr^{-1}$, respectively. NEPs of P. thunbergii and C. crenata stands were shown in $2.5ton\;C\;ha^{-1}yr^{-1}$ and $1.8ton\;C\;ha^{-1}yr^{-1}$, respectively. Carbon absorption capacity for the whole set of vegetation types established in the NIE was estimated by applying NEP indices obtained from current study and extrapolating NEP indices from existing studies. The value was shown in $147.6ton\;C\;ha^{-1}yr^{-1}$ and it was calculated as $541.2ton\;CO_2ha^{-1}yr^{-1}$ converted into $CO_2$. This function corresponds to 62% of carbon emission from energy that NIE uses for operation of various facilities including the glass domes known in Ecorium. This carbon offset capacity corresponds to about five times of them of the whole national territory of Korea and the representative rural area, Seocheongun. Considered the fact that ongoing climate change was originated from imbalance of carbon budget at the global level, it is expected that evaluation on carbon budget in the spatial dimension reflected land use pattern could provide us baseline information being required to solve fundamentally climate change problem.

• Management & Information Systems Review
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• 제37권3호
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• pp.1-25
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• 2018

This research performed the empirical test for the effects of learning transfer on perceived usefulness, perceived ease of use, self-efficacy and work environment using 390 employees who have experienced e-learning in domestic and foreign companies. Analyzed the mediating effects of self-efficacy and work environment in addition to direct effect of each factor on learning transfer. The results showed that perceived usefulness and perceived ease-of-use of e-learning learner had a positive(＋) effect on self-efficacy and a positive influence on supervisor and peer support and organizational climate. Self-efficacy showed a positive effect on learning transfer, and supervisor support, peer support and organizational climate had a positive influence on learning transfer as well. Perceived usefulness also had a positive effect on learning transfer. However, perceived ease-of-use had no significant effect on learning transfer. As a result of the mediating effect analysis, self-efficacy and work environment were analyzed to have mediating effects between perceived usefulness, perceived ease of use, and learning transfer. The implications of this study are as follows. First, this study designed a new research model that reflects factors influencing the effect of learning transfer on acceptance of e-learning that is common in corporate education. It has derived a research model of perceived usefulness and perceived ease-of-use, which were used as mediating variables for external characteristics factors, as independent variables, using self-efficacy and work environment as mediating variables, which were studied as external factors. Second, most of the studies on technology acceptance model and learning transfer are conducted in a single country. The reliability was enhanced by testing the study models using different samples from 26 countries. Third, perceived usefulness and ease-of-use in existing studies have been considered as key determinants of acceptance intention and learning transfer. This study explored the mediating effects of learner and environmental factors on the accepted information technology and strengthened and supplemented the path of learning transfer of perceived usefulness and ease-of-use. In addition, based on the sample analysis of various countries used in this study, it is expected that future international comparative studies will be possible.

• KOREAN JOURNAL OF CROP SCIENCE
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• 제63권4호
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• pp.331-337
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• 2018

The content of nutrients, proteins, and oils of crop seeds is affected by global climate change due to the increase in temperature. Information regarding the effects of increased temperature on soybean seed nutrition is limited despite its vital role in seed quality and food security. The objective of this study was to determine the effect of increasing temperature on seed nutrient, protein, and oil content in two soybean [Glycine max (L.) Merr] cultivars (Daewonkong and Pungsannamulkong during the reproductive period in a temperature-gradient chamber. Four temperature treatments, Ta (near ambient temperature), $Ta+1^{\circ}C$ (ambient temperature+$1^{\circ}C$), $Ta+2^{\circ}C$ (ambient temperature+$2^{\circ}C$), $Ta+3^{\circ}C$ (ambient temperature+$3^{\circ}C$), and $Ta+4^{\circ}C$ (ambient temperature+$4^{\circ}C$), were established by dividing the rows along the temperature gradient. At maturity, increased temperature did not significantly affect the concentration of P, K, Ca, and Mg. The protein and oil content was significantly correlated with temperature. At maturity, the protein content of DWK and PSNK was reduced at $Ta+4^{\circ}C$. The oil content was the highest at $Ta+4^{\circ}C$ in DWK, whereas it decreased in PSNK at $Ta+4^{\circ}C$. Consequently, the biochemical composition of soybean seeds changed with the increase in temperature. These results illustrate the effects of temperature on soybean seed nutrient, protein, and oil content, which can help improve soybean quality at different temperatures. Thus, the biochemical composition of crop seeds can be changed in accordance with nutritional requirements for the benefit of human health in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• 제21권2호
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Korean Journal of Mineralogy and Petrology
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• 제34권1호
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• pp.15-29
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• 2021

In the Arctic Ocean, the distribution of sea ice and ice sheets changes as climate changes. Because the distribution of ice cover influences the mineral composition of marine sediments, studying marine sediments transported by sea ice or iceberg is very important to understand the global climate change. This study analyzes marine sediment samples collected from the Arctic Ocean and infers the provenance of the sediments to reconstruct the paleoenvironment changes of the western Arctic. The analyzed samples include four gravity cores collected from the Araon mound in the Chukchi Plateau and one gravity core collected from the slope between the Araon mounds. The core sediments were brown, gray, and greenish gray, each of which corresponds to the characteristic color of sediments deposited during the interglacial/glacial cycle in the western Arctic Ocean. We divide the core sediments into three units based on the analysis of bulk mineral composition, clay mineral composition, and Ice Rafted Debris (IRD) as well as comparison with previous study results. Unit 3 sediments, deposited during the last glacial maximum, were transported by sea ice and currents after the sediments of the Kolyma and Indigirka Rivers were deposited on the continental shelf of the East Siberian Sea. Unit 2 sediments, deposited during the deglacial period, were from the Kolyma and Indigirka Rivers flowing into the East Siberian Sea as well as from the Mackenzie River and the Canadian Archipelago flowing into the Beaufort Sea. Unit 2 sediments also contained an extensive amount of IRD, which originated from the melted Laurentide Ice Sheet. During the interglacial stage, fine-grained sediments of Unit 1 were transported by sea ice and currents from Northern Canada and the East Siberian Sea, but coarse-grained sediments were derived by sea ice from the Canadian Archipelago.

• Journal of the Korean earth science society
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• 제42권6호
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• pp.632-645
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• 2021

Satellite sea surface temperature (SST) composites provide important data for numerical forecasting models and for research on global warming and climate change. In this study, six types of representative SST composite database were collected from 2007 to 2018 and the characteristics of spatial structures of SSTs were analyzed in seas around the Korean Peninsula. The SST composite data were compared with time series of in-situ measurements from ocean meteorological buoys of the Korea Meteorological Administration by analyzing the maximum value of the errors and its occurrence time at each buoy station. High differences between the SST data and in-situ measurements were detected in the western coastal stations, in particular Deokjeokdo and Chilbaldo, with a dominant annual or semi-annual cycle. In Pohang buoy, a high SST difference was observed in the summer of 2013, when cold water appeared in the surface layer due to strong upwelling. As a result of spectrum analysis of the time series SST data, daily satellite SSTs showed similar spectral energy from in-situ measurements at periods longer than one month approximately. On the other hand, the difference of spectral energy between the satellite SSTs and in-situ temperature tended to magnify as the temporal frequency increased. This suggests a possibility that satellite SST composite data may not adequately express the temporal variability of SST in the near-coastal area. The fronts from satellite SST images revealed the differences among the SST databases in terms of spatial structure and magnitude of the oceanic fronts. The spatial scale expressed by the SST composite field was investigated through spatial spectral analysis. As a result, the high-resolution SST composite images expressed the spatial structures of mesoscale ocean phenomena better than other low-resolution SST images. Therefore, in order to express the actual mesoscale ocean phenomenon in more detail, it is necessary to develop more advanced techniques for producing the SST composites.

• Journal of Appropriate Technology
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• 제7권2호
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• pp.136-143
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• 2021

Pacific island countries, including Kiribati, are suffering from a shortage of essential resources as well as a reduction in their living space due to sea level rise and coastal erosion from climate change, groundwater pollution and vegetation changes. Global activities to solve these problems are being progressed by the UN's efforts to implement SDGs. Pacific island countries can adapt to climate change by using abundant marine resources. In other words, seawater plants can assist in achieving SDGs #2, #6 and #7 based on SDGs #14 in these Pacific island countries. Under the auspice of Korea International Cooperation Agency (KOICA), Korea Research Institute of Ships and Ocean Engineering (KRISO) established the Sustainable Seawater Utilization Academy (SSUA) in 2016, and its 30 graduates formed the SSUA Kiribati Association in 2017. The Ministry of Oceans and Fisheries (MOF) of the Republic of Korea awarded ODA fund to the Association. By taking advantage of seawater resource and related plants, it was able to provide drinking water and vegetables to the local community from 2018 to 2020. Among the various fields of education and practice provided by SSUA, the Association hope to realize hydroponic cultivation and seawater desalination as a self-support project through a pilot project. To this end, more than 140 households are benefiting from 3-stage hydroponics, and a seawater desalination system in connection with solar power generation was installed for operation. The Association grows and supplies vegetable seedlings from the provided seedling cultivation equipment, and is preparing to convert to self-support business from next year. The satisfaction survey shows that Tarawa residents have a high degree of satisfaction with the technical support and its benefits. In the future, it is hoped that SSUA and regional associations will be distributed to neighboring island countries to support their SDGs implementations.

• Journal of Intelligence and Information Systems
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• 제29권1호
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Journal of Korea Water Resources Association
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• 제56권9호
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• pp.543-551
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• 2023

Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.