• Title/Summary/Keyword: 중.단기적 기후 변동

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Did Fluvial Terrace of Mountain Streams in Korea Form in Each Glacial Stage? (우리나라 산지 하천의 하안단구는 매 빙기마다 형성되었는가?)

  • Lee, Gwang-Ryul
    • Journal of The Geomorphological Association of Korea
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    • v.26 no.3
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    • pp.19-30
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    • 2019
  • This study summarizes domestic and foreign previous works on fluvial terrace with absolute ages to discuss formative process of climatic terrace in Korea. Different from traditional climatic terrace model, approximately three quarters from foreign works have argued that formation of climatic terrace can be attributed to medium- and short-term climatic change or other environmental factors, rather than long-term climatic change of glacial and interglacial cycles. Based on previous works on fluvial terrace in Korea, it can be suggested that fluvial terrace in Korea formed not due to long-term climatic change of 100,000-year cycles related to glacial and interglacial cycles, but due to medium- and short-term climatic change or climatic event of tens of thousands of years related to intensity change in summer monsoon, one of the important factors affecting precipitation in Korea.

Impacts of Seasonal and Interannual Variabilities of Sea Surface Temperature on its Short-term Deep-learning Prediction Model Around the Southern Coast of Korea (한국 남부 해역 SST의 계절 및 경년 변동이 단기 딥러닝 모델의 SST 예측에 미치는 영향)

  • JU, HO-JEONG;CHAE, JEONG-YEOB;LEE, EUN-JOO;KIM, YOUNG-TAEG;PARK, JAE-HUN
    • The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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    • v.27 no.2
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    • pp.49-70
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    • 2022
  • Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

Baseflow Varaiance Analysis using Runoff-Groundwater Linkage Model (지표-지하수 연동모형을 활용한 기저유량 변동특성 파악)

  • Yang, Dongseok;Lee, Seoro;Kang, Taeseong;Shin, Minhwan;Lim, Kyoungjae
    • Proceedings of the Korea Water Resources Association Conference
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    • 2021.06a
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    • pp.401-401
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    • 2021
  • 최근 급격한 기후변화로 인한 기온, 강수량 등의 시·공간적 변화는 홍수, 가뭄 등과 같은 자연재해의 빈도와 규모를 증가시키고 있다. 특히 한강수계 주요 하천에서는 급격한 도시화 및 산업화로 인한 물 수요의 증가와 기후변화로 인한 강수량 감소 그리고 하천변 시설에서의 과다한 지하수 이용으로 인해 지하수위 변동이 발생하고 있다. 2017년 국가 지하수관측연보 및 지하수조사연보에 따르면 한강수계에 위치한 전체 569개 관정 중 암반층 관정과 충적층 관정의 최근 5년간 지하수위 평균 변동폭은 각각 3.91 m, 2.73 m로 조사되었으며, 10년 이상 장기관측 자료를 보유한 430개소 관정 중 228개소 관정에서 지하수위 하강 추세를 보이는 것으로 조사된 바 있다. 이처럼 강우나 하천수위 등 자연적인 원인과 양수, 유출 등 인위적인 원인에 의해 발생하는 지하수위의 하강은 지반 침하의 주요 원인이 되며, 하천 기저유출의 변화에도 큰 영향을 미치고 있다. 기저유출은 하천으로 단기 유출되는 지하수로 평수기 및 갈수기 하천 유량의 대부분을 차지하고 있기 때문에 건기시 하천 수질과 수생태계 관리에 있어 매우 중요한 요소에 해당된다. 따라서, 기후변화에 의한 이상가뭄 발생 등을 대비하기 위한 비상용수 또는 대체수자원으로서의 지하수 개발수요가 증가하는 추세에 따라 기저유량 확보 및 수질 개선 방안을 수립하는 것은 지속가능한 수자원 이용·관리 측면에 있어서 매우 중요하다. 현재 활용되는 SWAT(Soil and Water Assessment Tool)과 HSPF(Hydrological Simulation Program Fortran) 수문모형의 경우 지표유출 모의에 있어서 다양하게 활용되고 있으나 기저유량의 특성을 고려하기 위해서는 지표하 수문거동 모의가 어렵다는 한계가 있다. 또한 지표하 수문거동 모의가 가능한 MODFLOW의 경우 지표유출을 모의하기 어려운 한계가 있다. 이를 극복하기 위하여 SWAT-MODFLOW 모형이 개발되었으며, 본 연구에서는 SWAT-MODFLOW 모형을 활용하여 신둔천 유역을 대상으로 유량 및 지하수위 모의결과를 검보정하여 기저유량을 산정하고 변동특성을 분석하였다.

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Assessment of Water Use Vulnerability Considering Climate and Socioeconomic Changes in Han River Watershed (기후 및 사회·경제 변화를 고려한 한강 유역의 물이용 취약성 평가)

  • Park, Hyesun;Kim, Heey Jin;Chae, Yeora;Kim, Yeonjoo
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.37 no.6
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    • pp.965-972
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    • 2017
  • Assessment of vulnerability of water use to climate change include a variety of climate change scenarios. However, in most future vulnerability studies, only the climate change scenarios are used and not the future scenarios of social and economic indicators. Therefore, in this study, we applied the Representative Concentration Pathway (RCP) climate change scenario and Shared Socioeconomic reference Pathway (SSP) developed by IPCC to reflect the future. We selected indicators for estimating the vulnerability of water use, and indices were integrated with a multi-criteria decision making approach - Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The indicator data utilized national statistics and reports, social and economic scenarios, and simulated results from the Soil and Water Assessment Tool (SWAT) model which reflects climate change scenario. Finally, we derived the rankings of water use vulnerability for the short-term future (2020) and mid-term future (2050) within the Han River watershed. Generally, considering climate change alone and considering climate change plus social and economic changes showed a similar spatial distribution. In the future scenarios, the watershed rankings were similar, but showed differences with SSP scenario in some watersheds. Therefore, considering social and economic changes is expected to contribute to more effective responses to climate change.

A Development of Construction Industry Production Index(CIPI) with Temperature Effects (기온효과를 고려한 건설업생산지수 예측모델 개발)

  • Kim, Seok-Jong;Kim, Hyun-Woo;Chin, Kyung-Ho;Jang, Han-Ik
    • Korean Journal of Construction Engineering and Management
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    • v.14 no.5
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    • pp.103-112
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    • 2013
  • After 1990s, the influence of construction industry has been decreased on national economy and construction business condition has been changed on economic recession and boom repeatedly. Larger fluctuation of business condition makes a forecast of it to be more difficult. Uncertainty in business prediction results in damages on construction companies and stakeholders. Therefore, study on forecasting a construction business is very important. This study suggests the Construction Industry Production Index(CIPI) to predict a construction business in consider of temperature effects. The results show that construction business is much influenced by temperature effects certainly and GDP. With the CBFM, this study examines CIPI for 2013 with two scenarios: 1)with GDP growth rate of 3.5% 2)with GDP growth rate of 2.4%. Thus, CIPI would be used as the economic state index to display the construction business conditions. Also, CIPI will be utilized as basic methodology in the impact of climate change in the construction industry.

Characteristics of SWAP Index-based Drought-Flood Abrupt Alternation Events in the Han River Basin (SWAP 지수를 이용한 가뭄-홍수 급변사상의 특성 분석: 한강유역을 중심으로)

  • Son, Ho Jun;Lee, Jin-Young;Yoo, Jiyoung;Kim, Tae-Woong
    • Proceedings of the Korea Water Resources Association Conference
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    • 2021.06a
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    • pp.399-399
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    • 2021
  • 최근 전세계적으로 발생하고 있는 기후변화로 인해 가뭄, 홍수, 태풍 등 자연재해의 빈도가 증가하고 있다. 특히, 강수량의 변동성이 커지면서 가뭄과 홍수가 단기간에 번갈아 가며 발생하는 경우가 자주 발생하고 있다. 가뭄과 홍수가 짧은 기간 동안에 교차해서 발생하는 급변사상은 예측하기 어려우며, 갑작스럽게 중첩되는 재난으로 인명과 재산피해 뿐 아니라 생태계에까지 심각한 영향을 미칠 것이다. 본 연구에서는 일 강수량 자료를 바탕으로 표준가중평균강수지수(Standard Weighted Average Precipitation, SWAP)를 산정하고 한강 유역의 가뭄-홍수 급변사상에 대한 특성을 분석하였다. 1966년부터 2018년까지의 한강유역 중권역별 면적평균강수량과 가중치, 이전 강수량의 영향을 받는 일수를 바탕으로 SWAP를 산정하였다. SWAP 지수가 10일 연속 -1 미만일 때를 가뭄이라 정의하고, 이후 SWAP 지수가 7일 연속 0.5 이상이면 가뭄사상이 종료된다고 판정하였다. 또한 SWAP 지수가 10일 연속 +1 초과일 때를 홍수라고 정의하고, SWAP 지수가 7일 연속 -0.5 이하가 되면 홍수사상이 종료된다고 판정하였다. 가뭄-홍수 급변사상이란 가뭄의 종료시점과 홍수의 시작시점의 차이가 5일 이내일 경우에 해당한다. 급변사상의 전·후로 강수량이 얼마나 급격하게 차이 나는지를 판단하기 위하여 급변 시점 전·후 5일의 누적 SWAP 지수인 심각도 K(Severity)를 분석지표로 활용하였다. K를 통해 한강유역 가뭄-홍수 급변사상의 시·공간적 분포를 분석하고 미래의 급변사상의 발생가능성을 예측할 수 있다. 본 연구 결과, 한강 유역의 24개 중권역 중에서 18개의 중권역이 가뭄-홍수 급변사상의 심각도가 점점 상승하는 추세이고, 가장 심각도 상승폭이 높은 중권역은 홍천강(1014)으로 첫 사상인 1967년부터부터 2015년의 마지막 사상까지 약 55% 정도 상승하였다.

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Very Short- and Long-Term Prediction Method for Solar Power (초 장단기 통합 태양광 발전량 예측 기법)

  • Mun Seop Yun;Se Ryung Lim;Han Seung Jang
    • 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.

Review of the Weather Hazard Research: Focused on Typhoon, Heavy Rain, Drought, Heat Wave, Cold Surge, Heavy Snow, and Strong Gust (위험기상 분야의 지난 연구를 뒤돌아보며: 태풍, 집중호우, 가뭄, 폭염, 한파, 강설, 강풍을 중심으로)

  • Chang-Hoi Ho;Byung-Gon Kim;Baek-Min Kim;Doo-Sun R. Park;Chang-Kyun Park;Seok-Woo Son;Jee-Hoon Jeong;Dong-Hyun Cha
    • 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.