RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      정규지상관측자료를 이용한 기후변화 취약성 지수 산정 = Estimation of Climate Change Vulnerability Indicator Using Routine Meteorological Data

      한글로보기

      https://www.riss.kr/link?id=T12043259

      • 0

        상세조회
      • 0

        다운로드
      서지정보 열기
      • 내보내기
      • 내책장담기
      • 공유하기
      • 오류접수

      부가정보

      다국어 초록 (Multilingual Abstract)

      Recently climate change is recognized to be serious issue, causing severe flood, drought or deteriorate air pollution. Therefore, efforts to assess vulnerability to climate change have been made, and a process of theory development and assessment practice have been one of the key issues over the past decade, reflecting the principal point in the reports of the IPCC(Intergovernmental Panel on Climate Change).
      In this background, this study is performed to calculate climate change vulnerability index based on the exposure indicator from routinely observed meteorological data and air quality measurement such as O3 and PM10. When proxy variables setting, the sensitivity indicator and adaptive capacity indicator according to index of the leading research topics were set up to use as possible. Proxy variables of exposure indicator set up basis of leading research and add new proxy variables from standard of special weather report of Korea Meteorological Administration(KMA). Exposure indicators from meteorology data are analyzed for the period from 1978 to 2007, and used air quality data for the period from 1990 to 2006. The routinely observed meteorological data include data of temperature, precipitation, wind speed, effective humidity and the air quality measurement data include data of O3, PM10. Focus on the impact of climate change to the sensitivity indicator and climate exposure indicator (+) sign was given, the adaptive capacity indicator (-) sign was granted.
      Sensitivity indicator show high Chungnam in agriculture, and Jeonnam in forestry, and Jeonnam in ecosystem. When proxy variables is more connected with section, result of sensitivity index is the larger one. Proxy variables have an effect on each section. Adaptive capacity indicator is high in Kyonggi. Economic proxy variables such as GDP and financial self-help is high or high education-related proxy variables such as education expenditure and the percentage of school attendance shown that the adaptive capacity index were higher.
      The value of climate exposure indicator is influenced by various climatological index. The highest value of climate exposure indicator is value of Kyongnam area. The majority of proxy variables is high, especially proxy variable in connection with precipitation. The value of climate exposure indicator in Daegu, proxy variable is high in relation to temperature and PM10. The value of climate exposure indicator is the lowest in Incheon and Jeonbuk. Therefore we know that Kyongnam have a lots of risk about extremely climate, Incheon is the reverse.
      The resultant value of vulnerability-resilience indicator (VRI) is different from both area to area and from sector to sector according to three climate change adaptation factors; sensitivity, adaptive capacity, and climate exposure. Lower VRI means higher potential to cope with climate change, and higher VRI means lack of adapting ability to the climate change in this thesis. VRI shows good value in Kyonggi, Incheon, Ulsan in agriculture sector, and Kyonggi, Incheon, Kyongbuk in forestry sector, and Kyonggi, Incheon, Kyongbuk in ecosystem sector, respectively. Kyonggi show good VRI about all sections due to the fact that there are low climate exposure indicator and adaptive capacity indicator is very high in Kyonggi.
      This study pertains to the calculations of regional VRI in three sectors without any analysis of uncertainty and other error analysis. Thus it should be requiring further attention and additional studies i.e. principal component analysis before confidence of the peroxy variables in every sector can be claimed.
      번역하기

      Recently climate change is recognized to be serious issue, causing severe flood, drought or deteriorate air pollution. Therefore, efforts to assess vulnerability to climate change have been made, and a process of theory development and assessment prac...

      Recently climate change is recognized to be serious issue, causing severe flood, drought or deteriorate air pollution. Therefore, efforts to assess vulnerability to climate change have been made, and a process of theory development and assessment practice have been one of the key issues over the past decade, reflecting the principal point in the reports of the IPCC(Intergovernmental Panel on Climate Change).
      In this background, this study is performed to calculate climate change vulnerability index based on the exposure indicator from routinely observed meteorological data and air quality measurement such as O3 and PM10. When proxy variables setting, the sensitivity indicator and adaptive capacity indicator according to index of the leading research topics were set up to use as possible. Proxy variables of exposure indicator set up basis of leading research and add new proxy variables from standard of special weather report of Korea Meteorological Administration(KMA). Exposure indicators from meteorology data are analyzed for the period from 1978 to 2007, and used air quality data for the period from 1990 to 2006. The routinely observed meteorological data include data of temperature, precipitation, wind speed, effective humidity and the air quality measurement data include data of O3, PM10. Focus on the impact of climate change to the sensitivity indicator and climate exposure indicator (+) sign was given, the adaptive capacity indicator (-) sign was granted.
      Sensitivity indicator show high Chungnam in agriculture, and Jeonnam in forestry, and Jeonnam in ecosystem. When proxy variables is more connected with section, result of sensitivity index is the larger one. Proxy variables have an effect on each section. Adaptive capacity indicator is high in Kyonggi. Economic proxy variables such as GDP and financial self-help is high or high education-related proxy variables such as education expenditure and the percentage of school attendance shown that the adaptive capacity index were higher.
      The value of climate exposure indicator is influenced by various climatological index. The highest value of climate exposure indicator is value of Kyongnam area. The majority of proxy variables is high, especially proxy variable in connection with precipitation. The value of climate exposure indicator in Daegu, proxy variable is high in relation to temperature and PM10. The value of climate exposure indicator is the lowest in Incheon and Jeonbuk. Therefore we know that Kyongnam have a lots of risk about extremely climate, Incheon is the reverse.
      The resultant value of vulnerability-resilience indicator (VRI) is different from both area to area and from sector to sector according to three climate change adaptation factors; sensitivity, adaptive capacity, and climate exposure. Lower VRI means higher potential to cope with climate change, and higher VRI means lack of adapting ability to the climate change in this thesis. VRI shows good value in Kyonggi, Incheon, Ulsan in agriculture sector, and Kyonggi, Incheon, Kyongbuk in forestry sector, and Kyonggi, Incheon, Kyongbuk in ecosystem sector, respectively. Kyonggi show good VRI about all sections due to the fact that there are low climate exposure indicator and adaptive capacity indicator is very high in Kyonggi.
      This study pertains to the calculations of regional VRI in three sectors without any analysis of uncertainty and other error analysis. Thus it should be requiring further attention and additional studies i.e. principal component analysis before confidence of the peroxy variables in every sector can be claimed.

      더보기

      목차 (Table of Contents)

      • 제 1 장. 서 론 1
      • 제 2 장. 기후변화 취약성 지수(Climate Change Vulnerability Indicator) 4
      • 2.1 기후변화 취약성의 개념 4
      • 2.2 취약성 지수 산정에 관한 선행연구 6
      • 2.2.1 Moss et al.(2001) 7
      • 제 1 장. 서 론 1
      • 제 2 장. 기후변화 취약성 지수(Climate Change Vulnerability Indicator) 4
      • 2.1 기후변화 취약성의 개념 4
      • 2.2 취약성 지수 산정에 관한 선행연구 6
      • 2.2.1 Moss et al.(2001) 7
      • 2.2.2 Brooks et al.(2005) 12
      • 2.2.3 Wehbe et al.(2005) 18
      • 2.3 취약성 지수의 표준화 방법 19
      • 2.4 취약성 지수의 계산 20
      • 2.4.1 부호의 결정 20
      • 2.4.2 각 지수의 계산 21
      • 제 3 장. 자료 및 연구 방법 24
      • 3.1 이용자료 24
      • 3.2 취약성 지수 계산을 위한 분야별, 요소별 대리 변수의 선정 26
      • 제 4 장. 결과 및 분석 31
      • 4.1 민감도 31
      • 4.1.1 농업 31
      • 4.1.2 임업 32
      • 4.1.3 생태계 33
      • 4.2 적응능력 33
      • 4.3 기후노출 36
      • 4.3.1 기온 36
      • 4.3.2 강수 37
      • 4.3.3 풍속 및 상대습도 38
      • 4.3.4 오존(O3) 및 미세먼지(PM10) 38
      • 4.3.5 기후노출지수(Climate Exposure Index:CEI) 39
      • 4.4 취약성-탄력성 지수(VRI) 45
      • 4.4.1 농업 45
      • 4.4.2 임업 45
      • 4.4.3 생태계 46
      • 제 5 장. 결론 및 요약 49
      • 참고 문헌 53
      • Abstract 55
      더보기

      분석정보

      View

      상세정보조회

      0

      Usage

      원문다운로드

      0

      대출신청

      0

      복사신청

      0

      EDDS신청

      0

      동일 주제 내 활용도 TOP

      더보기

      주제

      연도별 연구동향

      연도별 활용동향

      연관논문

      연구자 네트워크맵

      공동연구자 (7)

      유사연구자 (20) 활용도상위20명

      이 자료와 함께 이용한 RISS 자료

      나만을 위한 추천자료

      해외이동버튼