• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.559-572
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• 2021

A survey was conducted to analyze water masses and spatial distributions of phytoplankton communities at 15 stations on the surface and chlorophyll a maximum layers (CML) in the western South Sea of Korea from September 8 to 9, 2021. As a result, water masses were classified into Coastal Waters (CW) with relatively low salinity, the Tsushima Warm Current (TWC) with high water temperature and high salinity, and mixed waters (MW) showing a mixture of these two water masses. Turbidity showed high concentration in both the surface and CML. The chlorophyll a concentration was as low as 0.90±0.43 ㎍ L^-1 in the surface, more than 1.1 ㎍ L^-1 in CW, around 1.0 ㎍ L^-1 in MW, and less than 0.5 ㎍ L^-1 in the TWC. CML was 1.64±0.54 ㎍ L^-1. Regarding species composition of phytoplankton communities, there were 57 species in 31 genera(diatoms, 57.8%; dinoflagellates, 35.1%; and other phytoflagellates, 7.1%). The phytoplankton standing crop had 4.6±7.6 cells mL^-1 in the surface, more than 30 cells mL^-1 in the CW, 2-5 cells mL^-1 in the MW, and less than 2 cells mL^-1 in the TWC. CML was slightly higher than the surface with a variation of 5.7±8.4 cells mL^-1. Dominant species were found to be Rhizosolenia flagilissima f. flagilissima, Skeletonema costatum-ls, and Nitzschia sp./ small size in the surface. For the CML Rh. flagilisima f. flagilissima showed a dominance of 12.0%. For the surface, the diversity variation was 2.36±0.40, which was high for TWC but low for MW. For CML, the diversity variation was 2.29±0.52, which was slightly lower than that of the surface. The dominance in the surface was 0.50±0.15, with a fluctuation range of more than 0.5 in MW and less than 0.5 in the TWC, which was different from the diversity. According to correlation analysis and principal component analysis (PCA), the presence of phytoplankton standing crops was high in CW but low in MW and TWC. That is, phytoplankton communities in early autumn were strongly affected by the expansion and mixing of water masses in western South Sea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.2
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• pp.87-97
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• 2023

In this study, we examined the effects of a large wildfire in the coastal area of Uljin-gun. The analysis of water quality and the dominant species, species composition, and community structure of marine algal was conducted quarterly in 2022 at Nagok (F-1), Hujeong (F-2), Bongpyeong (F-3), and Gongse Port (F-C) in Uljin-gun. As a result of water quality analysis, the pH, a factor of wildfire impact was 8.07~8.30 and 8.12~8.48 in surface and bottom layers, respectively, which are normal values in coastal waters of the East Sea, suggesting no direct impact from wildfires. By marine algal species composition, the coastal areas show the following order: Rhodophyta (58.1%) > Ochrophyta (25.8%) > Chlorophyta (14.5%) > Magnoliophyta (1.6%). By season, Undaria pinnatifida was the most dominant at Nagok and Hujeong in March and June, which in September and November, Gelidium elegans and Lithophyllum sp. were the most dominant in Bongpyeong and Gongse Port, respectively. In the cluster analysis, the stations were divided into two groups according to presence and absence of specific marine algal by season. The dominant species were U. pinnatifida, G. elegans and D. divaricata in group A, and Lithophyllum sp. was mainly present in group B. Thus, the species composition and group structure reflected the normal seasonal change pattern with water temperature variation and showed little significant difference from the control site, suggesting no direct effects of the wildfire on algae distribution in Uljin.

• Journal of Marine Life Science
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• v.2 no.2
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• pp.62-69
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• 2017

In order to assess the potential environmental factors in the vicinity of a fish cage farm, we investigated the biotic and abiotic factors during a short-term period in summer 2016 in two inner stations of Tongyeong Obi. High water temperature on August 10^th was apparent among the full depth of up to 29℃, which might have been related to the abnormally high temperatures of large amounts of the Changjiang River discharge along the Tongyeong coast. The concentration of nitrate+nitrite, ammonium, phosphate, and silicate ranged from 0.08 to 5.11 μM, 0.08 to 34.62 μM, 0.01 to 1.15 μM, and 1.46 to 31.79 μM, respectively. The nutrients were mainly supplied by precipitation and leaching from the bottom sediments in the fish culture farm area. It was not retained for a long duration because of the phytoplankton consumption and diffusion by water currents. The chlorophyll a concentration varied from 0.49 ㎍ l^-1 to 7.39 ㎍ l^-1. At that time, Chaetoceros debilis, C. pseudocurvisetus, and Pseudo-nitzschia delicatissima were rapidly proliferated and reached the level of 4.74 × 10⁹ cells l^-1. In particular, the lowest dissolved oxygen was recorded at 4.52 ㎍ l^-1 at the bottom layer after bloom. Therefore, even though phytoplankton blooms in summer have frequently occurred in a fish culture farm area, the oxygen-deficient environments were not found in neither the surface nor bottom layers, which implied that the water masses might be well exchanged from the mouth of the northwest and southeast between Obi and Mireuk Island in the study area.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.277-291
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• 2023

To analyze the crustal velocity structures beneath 21 broadband seismic stations in Gangwon Province, South Korea, we first applied the H-κ stacking method to 139 teleseismic event data (Mw ≥ 5.8 and the epicentral distance of 30° - 90°) occurring between March 18, 2019 and December 31, 2022 to estimate the Moho depths and V_p/V_s ratios beneath each station. The Moho depths and V_p/V_s ratios from the H-κ stacking method range from 24.9 to 33.2 km depth and 1.695 - 1.760, respectively, and the estimated V_p/V_s ratios were applied to the joint inversion of receiver functions and surface wave dispersion to obtain 1-D crustal velocity models beneath each station. The resulting Moho depths range from 25.9 to 33.7 km depth, similar to the results from the H-κ stacking method. Moho depth results from the both methods are generally consistent with Airy's isostasy. The 1-D crustal velocity models confirm that the existence of 2 km thick low-velocity layers with P-wave velocities of 5 km/s or less at some stations in the Taebaeksan basin, and at the stations CHNB and GAPB in northern Gangwon Province, which are located above the Cenozoic sedimentary layer. The station SH2B, although not overlying a sedimentary layer, has a low P-wave velocity near the surface, which is probably due to various factors such as weathering of the bedrock. We also observe a velocity inversion with decreasing velocity with depth at all stations within 4 - 12 km depths, and mid-crustal discontinuities possibly due to density differences in the rocks at around 10 km depth below some stations.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of the Korean earth science society
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• v.44 no.6
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• pp.643-654
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• 2023

The West Philippine Basin, an oceanic basin half the size of the Philippine Sea Plate, lies in the western part of the plate and south of the Korean Peninsula on the Eurasian Plate. It subducts beneath the Eurasian Plate and the Philippine Islands bordering the Ryukyu Trench and the Philippine Trench with 25-50% of this basin already consumed. However, the history of the opening of the basin's southern region has been a topic of debate. The non-transform discontinuity formed during the seafloor spreading is similar to the transform fault boundaries normally perpendicular to mid-ocean ridge axes; however, it was created irregularly due to ridge propagations caused by variations of mantle convection attributable to magma supply changes. By analyzing high-resolution multi-beam echo-sounding data, we confirmed that the non-transform discontinuity due to the propagating rift evolved in the entire basin and that the abyssal hill strike direction changed from E-W to NNW-SSE from the fossil spreading center. In the early stage of basin extension, the Amami-Sankaku Basin was rotated 90 degrees clockwise from its current orientation, and it bordered the Palau Basin along the Mindanao Fracture Zone. The Amami-Sankaku Basin separated from the Palau Basin while the spreading of the West Philippine Basin began with a counter-clockwise rotation. This indicates that the non-transform discontinuities formed by a sudden change in magma supply due to the drift of the Philippine Sea Plate and simultaneously with the rapid changes in the spreading direction from ENE-WSW to N-S. The Palau Basin was considered to be the sub-south of the West Philippine Basin, but recent studies have shown that it extends into an independent system. Evidence from sediment layers and crustal thickness hints at the possibility of its existence before the West Philippine Basin opened, although its evolution continues to be debated. We performed a combined analysis using high-resolution multi-beam bathymetry and satellite gravity data to uncover new insights into the evolution of the West Philippine Basin. This information illuminates the complex plate interactions and provides a crucial contribution toward understanding the opening history of the basin and the Philippine Sea Plate.

• Economic and Environmental Geology
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• v.56 no.5
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• pp.589-601
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• 2023

We compare high-resolution seabed bathymetry data and seafloor backscattering data acquired, using multi-beam, between 2018 and 2021 to understand topographic changes in the coastal area of Dokdo. The study area, conducted within a 500 m × 500 m in the southern coast between the islands where Dongdo Port is located, has been greatly affected by human activities, waves and ocean currents. The depth variations exhibit between 5 - 70 m. Irregular underwater rocks are distributed in areas with a depth of 20 m or less and 30 - 40 m. As a whole, water depth ranges similar in the east-west direction and become flatter and deeper. The bathymetry contour in 2020 tends to move south as a whole compared to 2018 and 2019. The south moving of the contours in the survey area indicates that the water depth is shallower than before. Since the area where the change in the depth occurred is mainly formed of sedimentary layers, the change in the coast of Dokdo were mainly caused by the inflow of sediments, due to the influence of wind and waves caused by these typhoons (Maysak and Haishen) in 2020. In the Talus area, which developed on the shallow coast between Dongdo and Seodo, the bathymetry changed in 2020 due to erosion or sedimentation, compared to the bathymetry in 2019 and 2018. It is inferred that the changes in the seabed environment occur as the coastal area is directly affected by the typhoons. Due to the influence of the typhoons with strong southerly winds, there was a large amount of sediment inflow, and the overall tendency of the changes was to be deposited. The contours in 2021 appears to have shifted mainly northward, compared to 2020, meaning the area has eroded more than 2020. In 2020, sediments were mainly moved northward and deposited on the coast of Dokdo by the successive typhoons. On the contrary, the coast of Dokdo was eroded as these sediments moved south again in 2021. Dokdo has been largely affected by the north wind in winter, so sediments mainly move southward. But it is understood that sediments move northward when affected by strong typhoons. Such continuous coastal change monitoring and analysis results will be used as important data for longterm conservation policies in relation to topographical changes in Dokdo.

• Korean Journal of Poultry Science
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• v.50 no.4
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• pp.203-212
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• 2023

This study conducted a diallel-crossbreeding test using four Korean native chicken parent stock lines (YC, YD, CK, and CF) to develop a native commercial layer with high egg-laying performance. A total of 312 chickens in six combinations were examined for various traits, including livability, body weight, age at first egg-laying, hen-day, and hen-housed egg production, egg weight, and egg quality, from hatching to 60 weeks of age. The results showed that the average survival rate was 77.1±18.8% with the YDYC combination having the highest survival rate along with excellent specific combining ability. The YDYC combination exhibited significantly higher body weight compared to the other combinations (P<0.01). The average age at first egg-laying was 121.3±2.5 days, with no significant difference between the combinations. The average hen-day egg production was 74.0±6.4%, and the hen-housed egg production was 181.4±33.8 eggs with the YDCF and YCCK combinations demonstrating the highest laying performance, while the YDYC and CKCF combinations had the lowest (P<0.05). Laying performance was more influenced by specific combining ability than general combining ability. The eggs from the YDYC combination were significantly lighter and had the darkest shell color (P<0.01), whereas the YDCF combination exhibited the thickest eggshells. There was no difference in internal egg quality among combinations, except the YDCF combination had the darkest yolk color. Overall, we concluded that the YCCK combination, characterized by high laying performance and livability, and the YDCF combination with high laying performance and good egg quality are the most desirable combinations for Korean native commercial layers.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.15 no.1
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• pp.41-50
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• 2010

We have developed an in-situ benthic chamber (BelcI) for use in coastal studies that can be deployed from a small boat. It is expected that BelcI will be useful in studying the benthic boundary layer because of its flexibility. BelcI is divided into three main areas: 1) frame and body chamber, 2) water sampler, and 3) stirring devices, electric controller, and data acquisition technology. To maximize in-situ use, the frame is constructed from two layers that consist of square cells. All electronic parts (motor controller, pA meter, data acquisition, etc.) are low-power consumers so that the external power supply can be safely removed from the system. The hydrodynamics of BelcI, measured by PIV (particle image velocimetry), show a typical "radial-flow impeller" pattern. Mixing time of water in the chamber is about 30 s, and shear velocity ($u^*$) near the bottom layer was calculated at $0.32\;cm\;s^{-1}$. Measurements of diffusivity boundary layer thickness showed a range of $180-230\;{\mu}m$. Sediment oxygen consumption rate, measured in-situ，was $84\;mmol\;O_2\;m^{-2}\;d_{-1}$, more than two times higher than on-board incubation results. Benthic fluxes assessed from in-situ incubation were estimated as follows: nitrate + nitrite = $0.18\;{\pm}\;0.07\;mmol\;m^{-2}\;d^{-1}$ ammonium $23\;{\pm}\;1\;mmol\;m^{-2}\;d^{-1}$ phosphate = $0.09\;{\pm}\;0.02\;mmol\;m^{-2}\;d^{-1}$ and silicate = $23\;{\pm}\;1\;mmol\;m^{-2}\;d^{-1}$.