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

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.

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
• 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