• Journal of Korea Water Resources Association
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• v.56 no.3
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• pp.173-184
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• 2023

Due to climate change, algal blooms frequently occur not only in Korea but also around the world, and the risk of toxicity of harmful algae has recently been issued. It is known that the representative harmful algae, cyanobacteria, are caused by the intersection of three factors: water temperature, residence time, and nutrients. In this study, water quality simulation was carried out using EFDC, a three-dimensional numerical model, to analyze the variations in water quality due to the decrease of residence time according to the opening of Yeongju Dam in Naeseong-Cheon. In fact, the concentration of chlorophyll-a in Yeongju Dam in the summer of 2021 was significant, exceeding the 'algae warning' for a long time based on the previous algae warning system. On the other hand, as a result of performing the simulation under the condition that the dam gate was completely opened, the concentration of chlorophyll-a was mostly reduced below the 'algae warning' level during the simulation period. It was confirmed that reducing the residence time by restoring the flow of Naeseong-Cheon is a way to immediately reduce algae in Yeongju Dam.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.111-111
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• 2021

메콩지역은 최근 연 7%에 육박하는 경제성장률을 달성하며 아세안의 고성장을 지속 견인하고 있으나, 기후변화 및 급속한 도시화로 매년 가뭄·홍수 등 물 관련 재해 발생 빈도 및 강도 증가와 이에 따른 상·하류 국가간 물 분쟁 등으로 인해 메콩지역 지속가능 발전에 지장이 초래되고 있다. 이에 한국과 미국은 메콩우호국(Friends of the Lower Mekong, FLM) "메콩지역 수자원 데이터 관리 및 정보공유 강화에 관한 공동성명(2018년 8월)"을 계기로 메콩유역의 실시간 수자원 변동 모니터링 및 분석과 수자원 데이터 공동활용 역량을 강화하여 효율적이고 과학적인 수자원관리 지원과 함께 한국의 신남방정책과 미국의 인도-태평양 전략 시너지효과를 극대화하고자 메콩 주변국 재해경감 및 수자원 데이터 활용 역량강화를 위한 글로벌 위성기반 수문자료의 생산·활용 및 홍수·가뭄 등의 수재해 분석기술을 개발하고 있다. 여기에는 한국 K-water의 물관리 기술과 미국 NASA, USACE의 위성활용 및 수자원분석 기술을 접목하여 메콩지역의 체계적인 물관리 및 재해로부터 안전성 확보 기여에 목표를 두고 연구를 진행 중에 있다. 본 연구에서는 전 세계적으로 광범위하게 활용되고 있는 미공병단(USACE, U.S. Army Corps of Engineers)의 HEC software 프로그램을 메콩 시범지역(pilot area)에 적용하여 수리/수문모델 구축을 진행코자 한다. 구축되는 모형은 유역 상류 댐의 연계 모의운영 및 하류 홍수분석이 동시 가능한 HEC-RTS(Real-Time Simulation)로 이는 HEC-HMS, -ResSim, -RAS와 -FIA 모형이 순차적으로 결합된 수리/수문 모델링 시스템이다. 모형의 시범적용 지역은 현지 메콩위원회(MRC, Mekong River Comission)의 의견 등을 반영, 메콩강 하류지역(Lower Mekong) 본류 유역에 위성 자료 활용 및 준실시간(near real-time)으로 댐 모의운영 등을 고려할 수 있는 JingHong댐(중국 란창강 최하류)에서 라오스 Xayaburi댐(메콩강 최상류)까지의 구간을 선정하였다. 한편, 금번 연구에서는 HEC-RTS 중 HMS 모형 적용을 중심으로 가용한 위성자료(GPM IMERG)와 K-LIS 지표 모형 생산 자료를 활용하여 과거 홍수사상에 대한 모의를 고려하였다. 아울러, 연구에서 구축된 HMS 모형은 HEC-RTS에 포함되어 메콩 시범지역의 종합적 수리/수문분석에 적용될 예정이다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1143-1150
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• 2023

The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes to take long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R² values.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.4
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• pp.104-117
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• 2017

This study compares the results of landscape image analysis using text mining techniques and factor analysis for Lotte World Tower, which is the first atypical skyscraper building in Korea, and identifies landscape images of the site to determine possibilities of use. Lotte World Tower's landscape image has been extracted from text mining analysis focusing on adjectives such as 'new', 'transformational', 'unusual', 'novelty', 'impressive', and 'unique', and phrases such as in the process of change, people's active elements(caliber, outing, project, night view), media(newspaper, blog), and climate(weather, season). As a result of the factor analysis, factors affecting the landscape image of Lotte World Tower were symbolic, aesthetic, and formative. Identification, which is a morphological feature, has characteristics of scale and visibility but it is not statistically significant in preference. Rather, the psychological factors such as the symbolism with characteristics such as poison and specialty, harmony with the characteristics of the surrounding environment, and beautiful aesthetic characteristics were an influence on the landscape image. The common results of the two research methods show that psychological characteristics such as factors that can represent and represent the city affect the landscape image more greatly than the morphological and physical characteristics such as location and location of the building. In addition, the text mining technique can identify nouns and adjectives corresponding to the images that people see and feel, and confirms the relationship between the derived keywords, so that it can focus the process of forming the landscape image and further the image of the city. It would appear to be a suitable method to complement the limitation of landscape research. This study is meaningful in that it confirms the possibility that big data can be utilized in landscape analysis, which is one research field of landscape architecture, and is significant for understanding the information of a big data base and contribute to enlarging the landscape research area.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.5
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• pp.523-530
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• 2020

Based on the results of harmful algal blooms (HABs) monitoring by the National Institute of Fisheries Science and local governments, the effects of changes in the marine environment on HABs are described. Since the beginning of HABs monitoring in 1972, they continued to increase from the 1980s to the 1990s. After the largest number of HAB incidents (109) in 1998; the trend declined until the 2010s. Most HABs in the 1970s were caused by diatoms. In the 1980s, coastal dinoflagellates caused HABs; Cochlodinium polykrikoides blooms have been occurring continuously since 1993. There are three HAB species that cause damage to fisheries in Korea. The high-density bloom of Karenia mikimotoi caused mass mortality in shellfish in Jinhae Bay in 1981. Karenia sp. blooms occurring around Tongyeong in 1992 killed aquaculture fish. Since the occurrence of the largest fisheries damage of KRW 76.6 billion in 1995 caused by C. polykrikoides blooms, they have been occurring continuously. The concentration of nutrients in coastal waters was the highest in the 1980s and has declined since the mid-1990s. This reduction in nutrient concentration is a good explanation for the decreasing number of HABs. Since 2016, a summer high water temperature of 30℃ or more has appeared, and the range and scale of C. polykrikoides blooms have been greatly reduced. In 2016, K. mikimotoi blooms occurred around Wando, Jangheung and Goheung and small scale blooms of C. polykrikoides occurred around Yeosu. There were no C. polykrikoides blooms in 2017; however, Alexandrium affine blooms occurred from Yeosu to Tongyeong. There was a small-scale blooms of C. polykrikoides in 2018 compared to those in the previous years. Our results show that reduction in nutrients and the high water temperature owing to climate change are a good explanation for variation in HABs in Korean coastal waters.

• Korean Journal of Environmental Agriculture
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• v.33 no.2
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• pp.86-102
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• 2014

BACKGROUND: According to the types of human interference, there are various plants that have strong vitality and ability to breed in the dry field. Recently, climate change alters the geographical distribution and phenology of the plant species. So, we need to understand present occurrence pattern and ecological characteristics of these plants. METHODS AND RESULTS: The plant species data were obtained from 8 regions in Jeolla-do. Flora investigation was done from May 2013 to September 2013. Habitat type of dry field in Jeolla-do was classified into 3 types (inside of dry field: IDF, embankment around the end of a dry field: EDF, levee slope of dry field: LS). The vascular plants of study area were listed 296 taxa which contain 68 families, 203 genera, 244 species, 43 varieties and 9 forms. The vascular plants of three different habitat types were IDF 174 taxa, EDF 249 taxa and LS 136 taxa. The occurrence rate of Therophyte was arranged by the order of IDF(67.6%), EDF(51.9%), LS(54.3%). Naturalized rate was analysed as IDF 27.9%, EDF 21.0%, LS 18.6%. Urbanization index was analysed as IDF 11.8%, EDF 13.7%, LS 10.0%. CONCLUSION: With these results, we found that three habitat types were ecological difference affected by the human impacts. Also, we found environmental indicators through the ecological characteristics of flora for the type of habitat of dry field. These indicators will help assess the agriculture environmental variability and the floral change according to the climate change in dry field.

• Journal of Korea Water Resources Association
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• v.40 no.12
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• pp.921-930
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• 2007

The objective of this study is to present temporal-spatial variation of water resources on climate change impacts using the IPCC SRES A2 scenario and dynamical downscaling of the results (using the MM5 model with a resolution of 27km by 27km) at 139 sub-basins in Korea. The variation of runoff shows differences in the change of rate according to the each sub-basins and analysis durations. It has increased in the sub-basins located in Han river basin and east part of it, the other basins have decreased. In seasonal analysis, runoff in autumn and winter have increased, while in spring and summer have decreased. The results of frequency analyzing classified runoff(Low flow(Q$\leq$5mm), Normal flow(5$\geq$100mm)) show that low flow increase in most of the sub-basins for 2031-2060 and 2061-2090. In the case of high flow, it have higher frequency ranging from -100% to 500% than low flow. Regardless of the variation of mean runoff, maximum discharge appeared to be increase in process of time. The regression method is used to figure out the relationship between the rate of runoff change and mean temperature, mean precipitation under A2 scenario. The mean actual evapotranspirations from the regression equations increased by 3.4$\sim$5.3% for the change of $1^{\circ}C$. Also, for the precipitation change of $\pm$10%, runoff variety range is -18.2$\sim$+12.4% in Han River, -21.6$\sim$+14.6% in Nakdong River, -17.5$\sim$+11.5% in Gum River, -18.4$\sim$+10.6% in Sumjin River, -19.9$\sim$+12.7% Youngsan River basin.

• Atmosphere
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• v.33 no.2
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• pp.223-246
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• 2023

This paper summarized the research papers on weather extremes that occurred in the Republic of Korea, which were published in the domestic and foreign journals during 1963~2022. Weather extreme is defined as a weather phenomenon that causes serious casualty and property loss; here, it includes typhoon, heavy rain, drought, heat wave, cold surge, heavy snow, and strong gust. Based on the 2011~2020 statistics in Korea, above 80% of property loss due to all natural disasters were caused by typhoons and heavy rainfalls. However, the impact of the other weather extremes can be underestimated rather than we have actually experienced; the property loss caused by the other extremes is hard to be quantitatively counted. Particularly, as global warming becomes serious, the influence of drought and heat wave has been increasing. The damages caused by cold surges, heavy snow, and strong gust occurred over relatively local areas on short-term time scales compared to other weather hazards. In particularly, strong gust accompanied with drought may result in severe forest fires over mountainous regions. We hope that the present review paper may remind us of the importance of weather extremes that directly affect our lives.

• Korean Journal of Environmental Biology
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• v.25 no.1
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• pp.16-26
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• 2007

To study the succession of epilithic diatoms on the artificial substrate, we investigated environmental factors and the diatom assemblages biweekly from Mar. 2004 to Feb. 2005 at 2 stations in the lower part of the Han River. A total of 60 taxa, representing 2 orders, 3 suborders, 8 families, 17 genera, 51 species, 7 varieties and 2 forms were identified, and mean number of species were 19 species in spring, 20 in summer, 22 in autumn and 22 in winter. Standing crops of epilithic diatoms varied extensively by months and stations; mean values of those were $3.2{\times}10^4$ cells $cm^{-2}$ in spring, $1.9{\times}10^4$ in summer, $1.7{\times}10^4$ in autumn and $1.8{\times}10^5$ in winter. Chlorophyll a concentrations were also similarly showed as variations of the diatom assemblages. Succession of the diatoms in St. 1 was as follows; Melosira varians, Fragilaria capucina, Cyclotella comta, Nitzschia palea in spring, Fragilaria capucina in summer, Aulacoseira granulata var. angustissima in autumn, Aulacoseira granulata var. angustissima and Melosira varians, Cymbella minuta in winter. In station 2, Aulacoseira granulata and Nitzschia palea dominated in spring as a pioneer in early stage of succession, Fragilaria capucina in summer, and Nitzschia palea in winter. According to Canonical Correspondence Analysis (CCA), there showed similar to that of succession of epilithic diatoms within St. 1 and St. 2, and they were not changed by stations but seasons. Nitzschia palea belonged to saprophilous taxa correlated with nitrogen sources and suspended solids. Meanwhile, Fragilaria capucina and Cymbella minuta included in xenosaprobic taxa show correlation with DO and pH. Eurysaprobic taxa correlated with all environmental factors.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.