• Journal of the Korean earth science society
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• v.44 no.2
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• pp.135-147
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• 2023

Satellite-observed significant wave heights (SWHs), which are widely used to understand the response of the ocean to climate change, require long-term and continuous validation. This study examines the accuracy and error characteristics of SWH observed by nine satellite altimeters in the North Pacific and North Atlantic Ocean for 25 years (1992-2016). A total of 137,929 matchups were generated to compare altimeter-observed SWH and in-situ measurements. The altimeter SWH showed a bias of 0.03 m and a root mean square error (RMSE) of 0.27 m, indicating relatively high accuracy in the North Pacific and North Atlantic Ocean. However, the spatial distribution of altimeter SWH errors showed notable differences. To better understand the error characteristics of altimeter-observed SWH, errors were analyzed with respect to in-situ SWH, time, latitude, and distance from the coast. Overestimation of SWH was observed in most satellite altimeters when in-situ SWH was low, while underestimation was observed when in-situ SWH was high. The errors of altimeter-observed SWH varied seasonally, with an increase during winter and a decrease during summer, and the variability of errors increased at higher latitudes. The RMSEs showed high accuracy of less than 0.3 m in the open ocean more than 100 km from the coast, while errors significantly increased to more than 0.5 m in coastal regions less than 15 km. These findings underscore the need for caution when analyzing the spatio-temporal variability of SWH in the global and regional oceans using satellite altimeter data.

• Journal of Practical Agriculture & Fisheries Research
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• v.4 no.1
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• pp.80-88
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• 2002

This study was carried out to investigate the native distribution and flowering characteristics of Lycoris genus which is endemic species in Asia. This study was summarized as fellows: Native distribution of Lycoris genus was situated in latitude 37- 24 degrees with high humidity of coastline. Mininum temperature of native area was at -10℃ during winter season. The leaf of L. squamigera, L. koreana, L. sangunea, L. sprengeri, L. incanata and L. flavescens emergenced in spring. The leaf of L. radiata, L. rdiata var pumila, L. aurea, L. traubii, L. albiflora and L. houdyshelli emergenced in autum. Bulb of Lycoris genus show a sympodial branching system which is composed of 14-23 scales and 2.8-5.2 leaves per each bulb at flowering time. The flower shape of L. squamigera, L. Koreana, L, aurea, L. incanata, L. sprengeri, L. sanguinea and L. flavescens was trumpet. The flower of L. radiata. L. radiata var pumila, L. albiflora, L. houdchelli and L. traubii. was spider.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.177-185
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• 2008

Accurate monitoring of snow cover is a key component for studying climate and global as well as for daily weather forecasting and snowmelt runoff modelling. The few observed data related to snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) were built for 7 major watersheds in South Korea. The decrease pattern of SCA for time (day) was expressed as exponentially decay function, and the determination coefficient was ranged from 0.46 to 0.88. The SCA decreased 70% to 100% from the maximum SCA when 10 days passed.

• Journal of The Geomorphological Association of Korea
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• v.18 no.4
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• pp.271-282
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• 2011

The purpose of this study is to clarify the discharge patterns of Yongnup, Daeam-san. Many hydrographs were analyzed by the types of rising and falling stages, and the slope of those stages with the semi-log graph paper was a key point to distinguish the discharge patterns during rainstorms. The correlation between rainfall intensity and slopes of the second or third rising stage was higher than that between slopes of the first rising stage and rainfall intensity. Also, the coefficient of correlation between discharge decrement and the lapsed time from the peak to inflection point of hydrograph, during high water stages, was better than that during low water stages. The annual average discharge rate of Yongnup was 0.54 and the average direct runoff ratio was 0.14. The total discharge amount from Yongnup was about 410,000 tons for a water year, the monthly maximum amount emerged in September and the minimum amount was occurred in March. In summer, 37.7% was a seasonal maximum runoff ratio; on the other hand, 9.4% was a seasonal minimum runoff ratio in winter.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.525-535
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• 2023

In thi study, we unveil the intricate interplay among picophytoplankton (0.2-2 ㎛) communities, warming surface water temperatures, and major inorganic nutrients within the southwestern East Sea from 2003-2022. The observed surface temperature rise, reflecting global climate trends, defies conventional seasonal patterns in temperate seas, with highest temperatures in summer and lowest in spring. Concurrently, concentrations of major dissolved inorganic nutrient display distinct seasonality, with peaks in winter and gradually declining thereafter during spring. The time course of chlorophyll-a concentrations, a proxy for phytoplankton biomass, reveals a typical bimodal pattern for temperate seas. Notably, contributions from picophytoplankton exhibited a steady annual increase of approximately 0.5% over the study period, although the total chlorophyll-a concentrations declined slightly. The strong correlations between picophytoplankton contributions and inorganic nutrient concentrations is noteworthy, highlighting their competitively advantageous responsiveness to the shifting nutrient regime. These findings reflect significant ecological implications for the scientific insights into the marine ecosystem responses to changing climate conditions.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.13 no.4
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• pp.308-319
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• 2008

Temporal variations in plankton assemblages and environmental factors in Asan Bay and their relationships were examined with the data collected from February till early June, 2005. Seawater temperatures showed typical pattern of temporal change observed in temperate waters. Salinity variation was minor. Phytoplankton biomass showed two peaks, one in February only in the inner part of the bay and the other in May in the whole bay. Phytoplankton succession was clearly shown with the increase of seawater temperatures. Diatom (Bacillariophyceae) dominated in February, diatom and cryptomonads (Cryptophyceae) prevailed in May, and dinoflagellates (Dinophyceae) was most abundant in June. Spring bloom in Asan Bay occurred about one month earlier than those observed in temperate seas. Among the inorganic nutrients (N, P and Si), only silicate concentration showed a significant negative correlation with phytoplankton biomass, indicating the sink of this nutrient in the bay to be the uptake by phytoplankton. Nitrate concentration seemed to be a limiting factor in this bay during the study period. Mesozooplankton abundances showed a significant positive correlation with seawater temperatures and a significant negative correlation with phytoplankton biomass. Increase of mesozooplankton abundance followed phytoplankton increase with the time lag of about two months. This increase of zooplankton seemed to be the result of increased seawater temperatures and food.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.1
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• pp.1-13
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• 2024

The southwestern part of the East Sea is known to have a high primary productivity compared to those in the northern and eastern parts, which is attributed to nutrients supplies either by Tsushima Warm Current or by coastal upwelling. However, research on the biological pump in this area is limited. We developed machine learning models to estimate net community production (NCP), a measure of biological pump, with high spatial and time scales of 4 km and 8 days, respectively. The models were fed with the input parameters of sea surface temperature, chlorophyll-a, mixed layer depths, and photosynthetically active radiation and trained with observed NCP derived from high resolution measurements of surface O₂/Ar. The root mean square error between the predicted values by the best performing machine model and the observed NCP was 6 mmol O₂ m^-2 d^-1, corresponding to 15% of the average of observed NCP. The NCP in the central part of the Ulleung Basin was highest in March at 49 mmol O₂ m^-2 d^-1 and lowest in June and July at 18 mmol O₂ m^-2 d^-1. These seasonal variations were similar to the vertical nitrate flux based on the ³He gas exchange rate and to the particulate organic carbon flux estimated by the ²³⁴Th disequilibrium method. To expand this method, which produces NCP estimate for spring and summer, to autumn and winter, it is necessary to devise a way to correct bias in NCP by the entrainment of subsurface waters during the seasons.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.156-168
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• 2024

Purpose: The trend of highway traffic accidents shows a repeating pattern of increase and decrease, with the fatality rate being highest on highways among all road types. Therefore, there is a need to establish improvement measures that reflect the situation within the country. Method: We conducted accident severity analysis using Random Forest on data from accidents occurring on 10 specific routes with high accident rates among national highways from 2019 to 2021. Factors influencing accident severity were identified. Result: The analysis, conducted using the SHAP package to determine the top 10 variable importance, revealed that among highway traffic accidents, the variables with a significant impact on accident severity are the age of the perpetrator being between 20 and less than 39 years, the time period being daytime (06:00-18:00), occurrence on weekends (Sat-Sun), seasons being summer and winter, violation of traffic regulations (failure to comply with safe driving), road type being a tunnel, geometric structure having a high number of lanes and a high speed limit. We identified a total of 10 independent variables that showed a positive correlation with highway traffic accident severity. Conclusion: As accidents on highways occur due to the complex interaction of various factors, predicting accidents poses significant challenges. However, utilizing the results obtained from this study, there is a need for in-depth analysis of the factors influencing the severity of highway traffic accidents. Efforts should be made to establish efficient and rational response measures based on the findings of this research.

• Korean Journal of Ecology and Environment
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• v.57 no.2
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• pp.61-74
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• 2024

Daemadeung, located in the estuary of the Nakdong River, is formed by sand dunes and possesses well-developed intertidal flats. This study aimed to investigate the habitat of benthic microalgae, photosynthetic pigments, and photosynthetic efficiency in the intertidal flats of Daemadeung from January to December 2011. The inorganic nitrogen content in the sediment pore water was primarily composed of ammonium, while nitrate + nitrite was dominant in the upper layer water. The concentration of chlorophyll a and fucoxanthin in the sediment surface was significantly higher than the mean of all the sediment layer. The average Fv/Fm of benthic microalgae during the entire survey period was 0.52±0.03, with the highest value (0.61±0.08) observed in February. The rETR_max showed a seasonal trend, being high from spring to early autumn (April to October) and low from winter to early spring (January to March, November, December), with the highest value (153.05±2.30 µmol electrons m^-2 s^-1) in July and the lowest (38.49±5.17 µmol electrons m^-2 s^-1) in January. The average Fv/Fm of diurnal microalgae was 0.48±0.03, with the highest value (0.61±0.08) observed at noon. The rETR_max showed a highest peak at noon (54.24±11.35 µmol electrons m^-2 s^-1) and reached its lowest point at 16:00 (26.17±4.75 µmol electrons m^-2 s^-1). These findings suggest that the productivity of benthic microalgae varies significantly depending on the survey time and sediment depth. Therefore, to quantify the productivity of benthic microalgae using Diving-PAM, surveys should be conducted based on tidal conditions, and simultaneous pigment analysis of sediment layers should also be performed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.4
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• pp.443-463
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• 2024

This study sought to improve the accuracy of precipitation prediction by utilizing monthly precipitation data for each region over the past 30 years. Using statistical models (ARIMA, SARIMA) and deep learning models (LSTM, GBM), we learned monthly precipitation data from 1983 to 2012 in Gangneung, Gwangju, Daegu, Daejeon, Busan, Seoul, Jeju, and Chuncheon. Based on this, monthly precipitation was predicted for 10 years from 2013 to 2022. As a result of the prediction, most models accurately predicted the precipitation trend, but showed a tendency to underpredict the actual precipitation. To solve these problems, appropriate models were selected for each region and season. The LSTM model showed suitable results in Gangneung, Gwangju, Daegu, Daejeon, Busan, Seoul, Jeju, and Chuncheon. When comparing forecasting power by season, the SARIMA model showed particularly suitable forecasting performance in winter in Gangneung, Gwangju, Daegu, Daejeon, Seoul, and Chuncheon. Additionally, the LSTM model showed higher performance than other models in the summer when precipitation is concentrated. In conclusion, closely analyzing regional and seasonal precipitation patterns and selecting the optimal prediction model based on this plays a critical role in increasing the accuracy of precipitation prediction.