• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.526-526
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• 2015

우리나라는 국토의 대부분이 산악으로 이루어진 지형학적 특성과 여름철에 비가 집중된다는 기상학적 요인으로 인해 물관리가 어려운 편이다. 최근에는 기후변화로 인한 이상 기상 현상으로 돌발성 호우와 가뭄 등의 발생 빈도가 증대되면서 용수공급 관리는 더욱 더 어려움을 겪고 있다. 이러한 가운데 장기 기상정보는 안정적인 이수기 용수 공급을 위한 댐 수위 운영 및 홍수기 운영 목표 수위 계획 수립 등에 활용도가 매우 높다. 최근 기상청은 2014년 6월 이후부터 기존의 장기예보를 확률 예보 방식으로 변경하면서 기온과 강수량에 대하여 평년 대비 높음(많음), 비슷, 낮음(적음)으로 단순 예보하는 기존의 방식에서 발생가능성에 대해 카테고리 별로 확률(%)을 발표하고 있다. 기후변화의 불확실성이 증가하는 가운데 개정된 새로운 형태의 확률장기예보를 물관리에 정량적으로 적용하여 보다 정확도 높은 중장기 물관리 체계가 구축되어야 할 것이다. 본 연구는 현재 기상청에서 제공하는 확률장기예보를 실제 댐 운영에 적용하기 위한 연구로서 과거 자료와 확률장기예보를 조합하여 2014년 6월~2015년 2월의 유역 강수량의 확률 분포를 전망하였다. 대상 지역은 안동댐 유역으로 과거 자료는 최근린법에 기초한 기상청 산하 관측소인 안동, 태백, 봉화, 영주의 1986~2013년의 월 자료를 사용하였고, Thissen법을 근거로 유역 강수량을 계산하였다. 확률장기예보는 안동댐 유역을 포함하는 대구 경북지역을 대상으로 한 동일한 기간의 예보 자료를 활용하였다. 과거 강수량은 각 월별로 적합도 검정 후 Gamma분포를 채택하였으며 이를 기반으로 예보의 카테고리 별 기준값을 산정한 후 장기예보의 확률정보를 조합하여 강수량의 확률 분포를 작성하였다. 이를 2014년 6월~2015년 2월의 실제 강수량과 비교한 결과 2014년 11월과 2015년 1월 경우 가장 큰 확률의 카테고리 강수 범위 안에 실제 강수량이 포함되었으나 나머지 월에서는 실제 값과 카테고리 확률 간에 상이한 결과를 보였다. 본 연구는 예보 자료 수의 제한 및 안동댐과 예보 구역의 지역 차에 의한 자료 차이 등이 배제되어 있기 때문에 참고 자료로만 활용 될 수 있을 것이라고 판단되며, 확률장기예보 정보를 이용하여 유역 강수량의 확률 분포를 산정함으로서 물관리 부문에서 예보의 정량적 적용 가능성을 최초로 제시했다는 것에 의의가 있다. 추후 기후 모델 특성과 확률장기예보 산출 기법 등을 보다 심도 깊게 고려하여 정확도 개선에 대한 연구가 보완되어야 할 것으로 판단된다.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.25-36
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• 2020

An increasing frequency and intensity of natural disasters have been observed due to climate change. To better prepare for these, the MOIS (ministry of the interior and safety) announced a comprehensive plan for minimizing damages associated with natural disasters, including drought and heavy snowfall. The spatial-temporal pattern of snowfall is greatly influenced by temperature and geographical features. Heavy snowfalls are often observed in Gangwon-do, surrounded by mountains, whereas less snowfall is dominant in the southern part of the country due to relatively high temperatures. Thus, snow depth data often contains zeros that can lead to difficulties in the selection of probability distribution and estimation of the parameters. A generalized mixture distribution approach to a maximum snow depth series over the southern part of Korea (i.e., Changwon, Tongyeoung, Jinju weather stations) are located is proposed to better estimate a threshold (𝛿) classifying discrete and continuous distribution parts. The model parameters, including the threshold in the mixture model, are effectively estimated within a Bayesian modeling framework, and the uncertainty associated with the parameters is also provided. Comparing to the Daegwallyeong weather station, It was found that the proposed model is more effective for the regions in which less snow depth is observed.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1749-1760
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• 2022

In modern society, human and social damages caused by natural disasters and frequent disaster accidents have been increased year by year. Prompt access to dangerous disaster sites that are inaccessible or inaccessible using state-of-the-art Earth observation equipment such as satellites, drones, and survey robots, and timely collection and analysis of meaningful disaster information. It can play an important role in protecting people's property and life throughout the entire disaster management cycle, such as responding to disaster sites and establishing mid-to long-term recovery plans. This special issue introduces the National Disaster Management Research Institute (NDMI)'s disaster management technology that utilizes various Earth observation platforms, such as mobile survey vehicles equipped with close-range disaster site survey sensors, drones, and survey robots, as well as satellite technology, which is a tool of remote earth observation. Major research achievements include detection of damage from water disasters using Google Earth Engine, mid- and long-term time series observation, detection of reservoir water bodies using Sentinel-1 Synthetic Aperture Radar (SAR) images and artificial intelligence, analysis of resident movement patterns in case of forest fire disasters, and data analysis of disaster safety research. Efficient integrated management and utilization plan research results are summarized. In addition, research results on scientific investigation activities on the causes of disasters using drones and survey robots during the investigation of inaccessible and dangerous disaster sites were described.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.835-841
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• 2022

The water level of the dam reservoir is an important data in the operation of the dam. reservoir storage can be calculated by using water levels or prepared for disasters such as drought and floods. However, the water level is measured near the dam, making it difficult to represent a reservoir with a large area, and there is a high possibility that the water surface will be distorted due to discharge. Furthermore, the results of the survey showed that the water level of the reservoir is irregular rather than constant, and the water level of the reservoir is repeatedly falling and rising by section. In order to calculate such a complex and irregular representative water level, the water level observation density of the reservoir must be increased. In this study, we tried to derive the optimum water level observation density for Hwacheon Dam. A reasonable water level measurement density was derived by investigating the water level elevation of the reservoir and statistically analyzing it. The observation density may vary depending on the size of the reservoir, so the same analysis was conducted on the Goesan Dam and Boseonggang Dam. According to the results, four Hwacheon dams, three Goesan dams, and seven Boseong River dams are needed for observation density.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.2
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• pp.88-94
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• 2023

This study was conducted to analyze and compare the dry matter yield of Italian ryegrass (IRG) cultivated under monoculture and mixed culture system to recommend suitable varieties that can be cultivated. Italian ryegrass cultivars, Green Fram (GF, extremely early-maturing), Kowinearly (KE, early-maturing), Kowinmaster (KM, mild-maturing), and Hwasan 104 (H104, late-maturing), were used for mono or mixed cultivation. The average monthly temperature in Cheonan over the past 30 years tended to be similar, but that in November and March are judged to be abnormal weather. The dry matter yield of GF+H104 was significantly higher during harvest than that of GF (p<0.05). The dry matter yields of KE and KE+KM were significantly higher during harvest than the output standards of KE and KM. There was no significant difference between the dry matter yield of H104 and KM (p>0.05), but KM had the highest yield of 16,763.1 kg/ha. Analysis showed that the highest dry matter yield during IRG harvest was obtained under monoculture and KE+KM mixed culture. Because the occurrence frequency of abnormal weather such as drought during spring is increasing recently, it is judged that IRG cultivation using early and middle growth is necessary to prepare for abnormal weather.

• Smart Media Journal
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• v.12 no.10
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• pp.93-101
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• 2023

Climate change has emerged as a global problem, with frequent temperature increases, droughts, and floods, and it is predicted that it will have a great impact on the characteristics and productivity of crops. Cnidium officinale is used not only as traditionally used herbal medicines, but also as various industrial raw materials such as health functional foods, natural medicines, and living materials, but productivity is decreasing due to threats such as continuous crop damage and climate change. Therefore, this paper proposes a model that can predict the physiologically active ingredient index according to the climate change scenario of Cnidium officinale, a representative medicinal crop vulnerable to climate change. In this paper, data was first augmented using the CTGAN algorithm to solve the problem of data imbalance in the collection of environment information, physiological reactions, and physiological active ingredient information. Column Shape and Column Pair Trends were used to measure augmented data quality, and overall quality of 88% was achieved on average. In addition, five models RF, SVR, XGBoost, AdaBoost, and LightBGM were used to predict phenol and flavonoid content by dividing them into ground and underground using augmented data. As a result of model evaluation, the XGBoost model showed the best performance in predicting the physiological active ingredients of the sacrum, and it was confirmed to be about twice as accurate as the SVR model.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.755-770
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• 2023

Due to recent severe climate change, abnormal weather phenomena, and other factors, the frequency and magnitude of natural disasters are increasing. The need for disaster management using artificial satellites is growing, especially during large-scale disasters due to time and economic constraints. In this study, we have summarized the current status of next-generation medium-sized satellites and microsatellites in operation and under development, as well as trends in satellite imagery analysis techniques using a large volume of satellite imagery driven by the advancement of the space industry. Furthermore, by utilizing satellite imagery, particularly focusing on recent major disasters such as floods, landslides, droughts, and wildfires, we have confirmed how satellite imagery can be employed for damage analysis, thereby establishing its potential for disaster management. Through this study, we have presented satellite development and operational statuses, recent trends in satellite imagery analysis technology, and proposed disaster response strategies that utilize various types of satellite imagery. It was observed that during the stages of disaster progression, the utilization of satellite imagery is more prominent in the response and recovery stages than in the prevention and preparedness stages. In the future, with the availability of diverse imagery, we plan to research the fusion of cutting-edge technologies like artificial intelligence and deep learning, and their applicability for effective disaster management.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.765-774
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• 2023

Due to climatechange, precipitation variability has increased, leading to more frequentoccurrences of droughts and floods. To establish measures for managing waterresources in response to the increasing uncertainties of climate conditions, itis necessary to understand the variability of natural river discharge and theimpact of reservoir operation modeling considering dam inflow and artificialwater supply. In this study, an integrated rainfall-runoff and reservoiroperation modeling was applied to analyze the water supply reliability andflood risk for a multipurpose dam catchment under climate change conditions. Therainfall-runoff model employed was the modèle du Génie Rural à 4 paramètresJournalier (GR4J) model, and the reservoir operation model used was an R-basedmodel with the structure of HEC-Ressim. Applying the climate change scenariosuntil 2100 to the established integrated model, the changes in water supplyreliability and flood risk of the Happcheon Dam were quantitatively analyzed.The results of the water supply reliability analysis showed that under SSP2-4.5conditions, the water supply reliability was higher than that under SSP5-8.5conditions. Particularly, in the far-future period, the range of flood risk widened,and both SSP2-4.5 and SSP5-8.5 scenarios showed the highest median flood riskvalues. While precipitation and runoff were expected to increase by less than10%, dam-released flood discharge was projected to surge by over 120% comparedto the baseline

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.5
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• pp.255-264
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• 2017

To evaluate the water quality changes in agricultural reservoir covered with floating photovoltaic solar-tracking systems, the water quality variations with time and depth were monitored on both six sites for light blocking zones and four sites for light penetration zones after the installation of floating photovoltaic solar-tracking systems in Geumgwang reservoir at Anseong-si, Kyeonggi province. For one year with 16 monitoring events, water quality parameters [i.e., water temperature, pH, dissolved oxygen (DO), chlorophyll-a (Chl-a), and blue-green algae (BGA)] were monitored at depths of 0.3 m, 1 m, 3 m, and 5 m, while chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) were monitored at depths of 0.3 m. Statistically, the difference in all water quality parameters was not significantly different (p > 0.05) at the level of significance of 0.05. Based on these results, the water quality data from light blocking zones (site 1~6) and light penetration zones (site 7~10) were clustered, and were compared with time and depth. As a result, the difference in water temperature, pH, DO, COD, TN, TP, Chl-a, and BGA between light blocking zones and light penetration zones was not significant (p > 0.05) with different time and depth. For Chl-a and BGA, some data from light blocking zones greater than light penetration zones were temporary observed due to the severe drought, low water storage rate, and over growth of periphyton. However, this temporal phenomenon did not impact the water quality. Considering the small water surface area (${\leq}0.5%$) covered by floating photovoltaic solar-tracking systems, the mixing effect of whole Geumgwang reservoir caused by Ekman current and continuous discharge were more dominant than the effect of reduced solar irradiance. Further study is warranted to monitor the changes in water quality and aquatic ecosystems with greater water surface area covered by floating photovoltaic solar-tracking systems for a long time.