• Title/Summary/Keyword: 기후환경조건

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A geologic - environmental study of Gosu Karst Cave

  • Yoo, Jae-Shin
    • Journal of the Speleological Society of Korea
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    • v.34 no.35
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    • pp.43-51
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    • 1993
  • Gosu Karst Cave 지역은 우리나라에서도 Karst rocks가 가장 잘 발달된 곳이며 특히 이 지역은 고생대의 조선계 대석회암통에 해당된다. Karst Cave와 여러가지 형태의 생성물은 지질시대부터 생성에 알맞는 고기후학적인 조건과 지구화학적인 여건이 결합하여 풍화작용의 결과로 이루어진 것이다. Cave의 발달 방향은 지층의 주향과 거의 일치하며 지금도 이러한 형성작용은 진행되고 있다. Cave 내부의 기후환경의 영향으로 Cave내에 서식하는 생물은 7목 9종이 확인 및 채집되었다.

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Analysis of spatio-temporal variation on water quality using hidden Markov model (은닉 마코프 모형을 이용한 시공간적 수질 변동성 분석)

  • Jung, Min-Kyu;Cho, Hemie;Kwon, Hyun-Han
    • Proceedings of the Korea Water Resources Association Conference
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    • 2020.06a
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    • pp.111-111
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    • 2020
  • 하천환경과 기후의 변화로 인해 수질오염 과정의 메커니즘이 더욱 복잡해짐에 따라 다양한 요인을 고려한 불확실성 평가 연구가 요구되고 있다. 하천 수질 중에서도 부영양화 문제는 특히 개발로 인한 하천환경 변화 이후 사회 정치적 논점이 되어왔다. 본 연구에서는 지난 7년 동안의 수질 변화의 전반적인 양상을 조사하였으며, 클로로필-a(Chl-a, chlorophyll-a) 농도의 시공간적 의존성의 효과적으로 고려하기 위해 기계학습 기반 분류(classification) 접근법인 다변량 은닉 마코프 모형(MHMM, multivariate hidden Markov model)을 사용하였다. 월 단위 수질 및 수문 자료를 사용하여 Chl-a의 변동성을 군집화하여 수질 상태의 익월 천이확률을 효과적으로 추정하였다. Chl-a와 수질 및 수문기상 조건의 관계를 평가하였으며, 결과적으로 수질 상태의 시공간적 전이가 정확하게 식별되었고 이의 잠재적 원인에 대하여 논의하였다.

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Assessment of water supply reliability in the Geum River Basin using univariate climate response functions: a case study for changing instreamflow managements (단변량 기후반응함수를 이용한 금강수계 이수안전도 평가: 하천유지유량 관리 변화를 고려한 사례연구)

  • Kim, Daeha;Choi, Si Jung;Jang, Su Hyung;Kang, Dae Hu
    • Journal of Korea Water Resources Association
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    • v.56 no.12
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    • pp.993-1003
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    • 2023
  • Due to the increasing greenhouse gas emissions, the global mean temperature has risen by 1.1℃ compared to pre-industrial levels, and significant changes are expected in functioning of water supply systems. In this study, we assessed impacts of climate change and instreamflow management on water supply reliability in the Geum River basin, Korea. We proposed univariate climate response functions, where mean precipitation and potential evaporation were coupled as an explanatory variable, to assess impacts of climate stress on multiple water supply reliabilities. To this end, natural streamflows were generated in the 19 sub-basins with the conceptual GR6J model. Then, the simulated streamflows were input into the Water Evaluation And Planning (WEAP) model. The dynamic optimization by WEAP allowed us to assess water supply reliability against the 2020 water demand projections. Results showed that when minimizing the water shortage of the entire river basin under the 1991-2020 climate, water supply reliability was lowest in the Bocheongcheon among the sub-basins. In a scenario where the priority of instreamflow maintenance is adjusted to be the same as municipal and industrial water use, water supply reliability in the Bocheongcheon, Chogang, and Nonsancheon sub-basins significantly decreased. The stress tests with 325 sets of climate perturbations showed that water supply reliability in the three sub-basins considerably decreased under all the climate stresses, while the sub-basins connected to large infrastructures did not change significantly. When using the 2021-2050 climate projections with the stress test results, water supply reliability in the Geum River basin was expected to generally improve, but if the priority of instreamflow maintenance is increased, water shortage is expected to worsen in geographically isolated sub-basins. Here, we suggest that the climate response function can be established by a single explanatory variable to assess climate change impacts of many sub-basin's performance simultaneously.

Remote Sensing Applications for Malaria Research : Emerging Agenda of Medical Geography (원격탐사 자료를 이용한 말라리아 연구 : 보건지리학적 과제와 전망)

  • Park, Sunyurp
    • Journal of the Korean association of regional geographers
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    • v.18 no.4
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    • pp.473-493
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    • 2012
  • Malaria infection is sensitively influenced by regional meteorological conditions along with global climate change. Remote sensing techniques have become an important tool for extraction of climatic and environmental factors, including rainfall, temperature, surface water, soil moisture, and land use, which are directly linked to the habitat qualities of malaria mosquitoes. Improvement of sensor fidelity with higher spatial and spectral resolution, new multinational sensor development, and decreased data cost have nurtured diverse remote sensing applications in malaria research. In 1984, eradication of endemic malaria was declared in Korea, but reemergence of malaria was reported in mid-1990s. Considering constant changes in malaria cases since 2000, the epidemiological management of the disease needs careful monitoring. Geographically, northmost counties neighboring North Korea have been ranked high in the number of malaria cases. High infection rates in these areas drew special attention and led to a hypothesis that malaria dispersion in these border counties might be caused by north-origin, malaria-bearing adult mosquitoes. Habitat conditions of malaria mosquitoes are important parameters for prediction of the vector abundance. However, it should be realized that malaria infection and transmission is a complex mechanism, where non-environmental factors, including human behavior, demographic structure, landscape structure, and spatial relationships between human residence and the vector habitats, are also significant considerations in the framework of medical geography.

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Development and Evaluation of Flood Prediction Models Using Artificial Intelligence Techniques (인공지능 기법을 활용한 홍수예측모델 개발 및 평가 - 한강수계 댐을 중심으로 -)

  • Cho, Hemie;Uranchimeg, Sumiya;Yoo, Je-Ho;Kwon, Hyun-Han
    • Proceedings of the Korea Water Resources Association Conference
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    • 2022.05a
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    • pp.131-131
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    • 2022
  • 기후변화의 영향으로 극치강우의 변동성이 커지고 있으며 계획빈도를 초과하는 폭우로 피해가 증가하고 있다. 기존의 물리기반의 홍수예측모델은 개념적 및 구조적 제약과 함께 다양한 유역조건 및 수문기상 조건에 기인한 강우-유출 관계의 불확실성을 고려하는 데 한계가 있다. 특히 한정된 홍수 사상을 통해 구축된 관측 자료로 인해 새로운 홍수 사상 예측 능력이 저조할 수밖에 없다. 따라서 기존 물리모형 기반의 홍수예측과 함께, 딥러닝(deep learning) 모형을 고려한 홍수예측 모델 개발과 개선이 필요하다. 본 연구에서는 다양한 분야에서 활용되는 인공지능(artificial intelligence, AI) 기술을 종합적으로 검토하고, 홍수 예측 측면에서의 활용 가능성 및 신뢰성을 고려하여 AI 기법을 채택하였다. 한강수계에 존재하는 댐 중 일부를 선정하여 대상 댐의 수문·기상학적 자료를 전처리한 후, 인공지능 기반의 홍수예측모형을 구축 및 최적화하였다. 다양한 예측인자와 모델 구성으로 홍수예측력에 대한 평가를 다각적으로 수행함으로써 홍수예측모델의 신뢰성을 제고하였다. 전반적으로 우수한 결과를 도출하였고, 유역면적이 작을수록 결과가 좋았다. 이는 넓은 유역일수록 복잡한 강우-유출 과정이 내재되어 있기 때문으로 판단되며, 넓은 유역에는 본 연구에서 활용한 자료에 추가적인 자료를 도입하여 모형 개선이 이루어져야 할 것으로 판단하였다. 수문 예측 연구에 통계모형이나 기계학습모형의 적용은 많이 있었지만, 딥러닝 기법 활용은 새로운 시도라는 점에서 의미가 있다.

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Development of Korean SPAR(Soil-Plant-Atmosphere-Research) System for Impact Assessment of Climate Changes and Environmental Stress (기후변화 및 환경스트레스 영향평가를 위한 한국형 SPAR(Soil-Plant-Atmosphere-Research) 시스템의 개발)

  • Sang, Wan-Gyu;Kim, Jun-Hwan;Shin, Pyong;Baek, Jae-Kyeong;Lee, Yun-Ho;Cho, Jung-Il;Seo, Myung-Chul
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.21 no.3
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    • pp.187-195
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    • 2019
  • The needs for precise diagnostics and farm management-decision aids have increased to reduce the risk of climate change and environmental stress. Crop simulation models have been widely used to search optimal solutions for effective cultural practices. However, limited knowledge on physiological responses to environmental variation would make it challenging to apply crop simulation models to a wide range of studies. Advanced research facilities would help investigation of plant response to the environment. In the present study, the sunlit controlled environment chambers, known as Korean SPAR (Soil-Plant-Atmosphere-Research) system, was developed by renovating existing SPAR system. The Korean SPAR system controls and monitors major environmental variables including atmospheric carbon dioxide concentration, temperature and soil moisture. Furthermore, plants are allowed to grow under natural sunlight. Key physiological and physical data such as canopy photosynthesis and respiration, canopy water and nutrient use over the whole growth period are also collected automatically. As a case study, it was shown that the Korean SPAR system would be useful for collection of data needed for understanding the growth and developmental processes of a crop, e.g., soybean. In addition, we have demonstrated that the canopy photosynthetic data of the Korean SPAR indicate the precise representation of physiological responses to environment variation. As a result, physical and physiological data obtained from the Korean SPAR are expected to be useful for development of an advanced crop simulation model minimizing errors and confounding factors that usually occur in field experiments.

Estimation and validation of the genetic coefficient of cv. Superior for the DSSAT-CSM (DSSAT 작물모형을 위한 수미품종의 품종모수의 결정과 기후변화에서의 활용)

  • Bak, Gyeryeong;Lee, Gyejun;Lee, Eunkyeong
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.20 no.2
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    • pp.166-174
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    • 2018
  • Potato(Solanum tuberosum L.) is one of the major food crop in the world following rice, wheat, and maize. It is thus important to project yield predict of potato under climate change conditions for assessment of food security. A crop growth modelling is widely used to simulate crop growth condition and total yield of various crops under a given climate condition. The decision support system for agrotechnology transfer (DSSAT) cropping system model, which was developed by U.S. which package integrating several models of 27 different crops, have been used to project crop yield for the impact assessment of climate change on crop production. In this study, we simulated potato yield using RCP 8.5 climate change scenario data, as inputs to the DSSAT model in five regions of Korea. The genetic coefficients of potato cultivar for 'superior', which is one of the most widely cultivated potato variety in Korea were determined. The GenCalc program, which is a submodule of the DSSAT package, was used to determine the genetic coefficients for the superior cultivar. The values of genetic coefficients were validated using results of 39 experiments performed over seven years in five regions. As a case study, the potato yield was projected that total yields of potato across five regions would increase by 26% in 2050s but decrease by 17% in 2090s, compared with 2010s. These results suggested that the needs for cultivation and irrigation technologies would be considerably large for planning and implementation of climate change adaptation for potato production in Korea.

The Impact of Land Use Structure and Vector Habitat Conditions on the Incidence of Malaria-A Case Study in High-Incidence Areas (매개모기의 서식환경과 토지이용 구조가 말라리아 발생에 미치는 영향 - 말라리아 고위험지역을 대상으로)

  • Kim, Ju-Hye;Park, Sun-Yurp
    • Journal of the Korean Association of Geographic Information Studies
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    • v.16 no.3
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    • pp.12-24
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    • 2013
  • Eradication of endemic malaria was declared in mid 1980's in Korea, but the number of malaria cases has been oscillating for the past 10 years since the reemergence of the disease in early 1990's. The occurrence of malaria has been concentrated near the demilitarized zone(DMZ), and the regional characteristics of the disease are evident. Considering the spatial variations of malaria incidence across the high-risk areas, the hotspot of the disease, it seems that the occurrence of the disease is influenced by the natural and human environment in the region. Malaria is an infectious disease that is transmitted to humans by the bites of vector-mosquitoes carrying malaria parasites, and it depends on specific climatic and sociodemographic factors. Malaria transmission is highly climate-sensitive, and temperature is the most important component. In addition, human contacts with vector-mosquitoes and the distance between human residence and mosquito habitats are crucial conditions determining malaria incidence rates. The present study aimed to test a hypothesis that the spatial characteristics of malaria incidence depended on local climatic conditions, relative proportions of mosquito habitats, and the distance between mosquito habitats and human residence using meteorological and satellite-based land cover data.

Trends identification of species distribution modeling study in Korea using text-mining technique (텍스트마이닝을 활용한 종분포모형의 국내 연구 동향 파악)

  • Dong-Joo Kim;Yong Sung Kwon;Na-Yeon Han;Do-Hun Lee
    • Korean Journal of Environmental Biology
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    • v.41 no.4
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    • pp.413-426
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    • 2023
  • Species distribution model (SDM) is used to preserve biodiversity and climate change impact. To evaluate biodiversity, various studies are being conducted to utilize and apply SDM. However, there is insufficient research to provide useful information by identifying the current status and recent trends of SDM research and discussing implications for future research. This study analyzed the trends and flow of academic papers, in the use of SDM, published in academic journals in South Korea and provides basic information that can be used for related research in the future. The current state and trends of SDM research were presented using philological methods and text-mining. The papers on SDM have been published 148 times between 1998 and 2023 with 115 (77.7%) papers published since 2015. MaxEnt model was the most widely used, and plant was the main target species. Most of the publications were related to species distribution and evaluation, and climate change. In text mining, the term 'Climate change' emerged as the most frequent keyword and most studies seem to consider biodiversity changes caused by climate change as a topic. In the future, the use of SDM requires several considerations such as selecting the models that are most suitable for various conditions, ensemble models, development of quantitative input variables, and improving the collection system of field survey data. Promoting these methods could help SDM serve as valuable scientific tools for addressing national policy issues like biodiversity conservation and climate change.

Long-Term Operation Modeling for the Hydropower Reservoir in the Han River Basin Using Linear Programming (선형계획법을 이용한 한강 수계 수력발전 댐 장기모형 구축)

  • Lee, Eunkyung;Ji, Jungwon;Yi, Jaeeung
    • Proceedings of the Korea Water Resources Association Conference
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    • 2015.05a
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    • pp.156-156
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    • 2015
  • 최근 화석연료의 사용으로 인한 지구온난화 등 환경파괴가 점점 증가하는 추세이며 이로 인해 신재생에너지 중 하나인 수력발전이 주목받고 있다. 수력발전은 물의 위치에너지를 기계에너지로 이를 다시 전기에너지로 변환하는 친환경적인 방식으로 운영된다. 수력발전량은 우리나라 전체 발전량의 1.5% 정도로 적은 양의 발전량을 생산하지만 가동시간이 짧아 전력수요가 급변하는 상황에 대비 가능하기 때문에 수력발전은 필수적이다. 기후변화의 영향으로 연평균강수량은 증가하는 양상을 보이나 연 강수일수는 줄어드는 등 수자원의 불확실성이 증가하고 있는 실정이다. 따라서 미래 불확실한 수자원 공급에 대비할 수 있는 수자원의 효율적 활용에 대한 연구가 필요하다. 본 연구에서는 하천의 유량이 계절에 따라 변동 폭이 크다는 점을 고려하며 월별 발전량을 최대화하기 위해 선형계획법을 적용하는 모형을 구축하였다. 선형계획법은 목적함수와 제약조건식 모두 1차식으로 비선형항을 포함할 수 없으나 초기 해가 불필요하고 최적해가 보장된다는 장점을 가진다. 일부 목적함수나 제약조건식에 비선형항이 포함되어 있을 경우 Successive Linear Programming(SLP), Piecewise Linear Programming(PLP), Taylor Expansion 등의 방법을 이용하여 선형화할 수 있다. 본 연구에서 비선형 제약조건은 Taylor Expansion을 이용하여 선형화하였으며 한강수계 9개 댐의 월간 발전량을 최대화시키는 장기 운영 모형을 구축하였다. 개발 환경은 Linux-CentOS이며 사용프로그램은 통계 분석에 많이 활용되는 R programming이다. R programming은 패키지를 이용한 개발이 용이하고 Windows 뿐만 아니라 Linux, Mac, Unix 등의 운영체제에서도 호환 가능하다는 장점이 있다.

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