Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
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Projection of Forest Vegetation Change by Applying Future Climate Change Scenario MIROC3.2 A1B

미래 기후변화 시나리오 MIROC3.2 A1B에 따른 우리나라 산림식생분포의 변화 전망

  • Shin, Hyung-Jin (Dept. of Civil and Env., System Eng. Konkuk University) ;
  • Park, Geun-Ae (Dept. of Civil & Env. Eng., University of Washington) ;
  • Park, Min-Ji (Dept. of Civil & Env. Eng., University of Massachusetts) ;
  • Kim, Seong-Joon (Dept. of Civil and Env., System Eng. Konkuk University)
  • 신형진 (건국대학교 사회환경시스템공학과) ;
  • 박근애 (워싱턴대학교 토목환경공학과) ;
  • 박민지 (메사추세츠주립대학 토목환경공학과) ;
  • 김성준 (건국대학교 사회환경시스템공학과)
  • Received : 2011.09.27
  • Accepted : 2012.02.06
  • Published : 2012.03.31
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Abstract

To predict the future distribution of forest vegetation, the present forest stand distributions of South Korea were represented by multinomial logit model with the following environmental variables: summer average precipitation, the coldest month average temperature, elevation, degree of base saturation, and soil organic matter. The future forest community was predicted by applying the MIROC3.2 hires A1B scenario. The future climate data were downscaled by statistically method. The coldest month average temperature increased $4.4^{\circ}C$, $6.0^{\circ}C$, and $9.4^{\circ}C$, and 3 months average precipitation changed -1.2%, 5.7%, and 5.3% for 2020s, 2050s, and 2080s respectively. For the projected summer precipitation and the coldest temperature, the future deciduous and mixed forests in the study area increased 56.9% and 8.3% and the coniferous forest decreased 11.2% in 2080s based on present.

미래 산림식생분포의 변화를 전망하기 위해 우리나라의 현재 산림식생분포도를 기준으로 환경변수와 다항로짓모델을 이용하였다. 환경변수는 지형, 최한월온도, 여름평균강수량, 토양염기포화율, 토양유기물함량 자료를 구축하였으며, MIROC3.2 hires A1B 시나리오 자료와 통계적 상세화 기법을 적용하여 미래기후변화시나리오를 구축하였다. 미래 여름강수량은 현재보다 각각 -1.2%, 5.7%, 5.3%로 증가추세였고, 미래 최한월온도는 2020s, 2050s, 2080s에 현재보다 각각 $4.4^{\circ}C$, $6.0^{\circ}C$, $9.4^{\circ}C$ 증가하였다. 미래 여름평균강수량과 최한월온도를 이용하여 미래 산림식생분포변화를 전망한 결과 현재 산림식생분포에 비해 2080s에 미래 활엽수림과 혼효림은 각각 56.9%와 8.3% 증가하고 미래 침엽수림은 11.2% 감소하는 것으로 전망되었다.

Keywords

Acknowledgement

Supported by : 한국건설교통기술평가원

References

  1. 김성준, 이용준. 2007. 면적규모 및 공간해상도가 CA-Markov 기법에 의한 미래 토지이용 예측결과에 미치는 영향. 한국지리정보학회지 10(2):57-69.
  2. 수자원의 지속적 확보기술개발사업단. 2009. 수자원의 지속적 확보기술개발 사업 3단계. 연구보고서.
  3. 윤진일, 이동석, 최재연, 조성인, 박은우, 황헌. 1999. 고도보정 공간내삽에 의한 전국의 $1{km^2}$단위 기상실황 추정. 농업정보과학 1(1): 28-33.
  4. 전성우, 박용철, 정휘철, Hideo Haradawa, Kiyoshi Takahashi. 2001. 기후변화에 따른 생태계 영향평가 및 대응방안연구 II -산림부분의 생태기후 모형을 개발을 중심으로- 한국환경정책평가연구원 .
  5. 한국수자원공사. 2000. 수자원계획의 최적화연구(IV): 기후변화에 따른 수자원계획의 영향 평가. 연구보고서 23-30쪽.
  6. 한국환경정책평가연구원. 2007. 기후변화 영향평가 및 적응시스템 구축 II.
  7. Bonan, G.B. 1996. A Land Surface Model (LSM version 1.0) for Ecological, Hydrological, and Atmospheric Studies: Technical Description and User's Guide. National Center for Atmospheric Research, Boulder, Colorado. 156pp.
  8. IPCC. 2007. Climate change 2007: Synthesis Report. IPCC Fourth Assessment Report. Geneva, Switzerland, 104pp.
  9. Kim, J.U. 2005. A study on the vulnerability assessment of forest vegetation using regional climate model. M.D. Univ. of Seoul, Seoul, South Korea. 79pp. (in Korean).
  10. Kim, K.Y., and G.R. North. 1997. EOFs of harmonizable cyclostationary processes. Journal of Atmospheric Science 54(19):2416-2427. https://doi.org/10.1175/1520-0469(1997)054<2416:EOHCP>2.0.CO;2
  11. Kim, N.S. 2008. Analysis of temporal and spatial distribution pattern for hanbando temperature using GIS. 2008 Annual Conference of the Korean Geographical Society:369-373 (in Korean).
  12. Korea Forest Research Institute. 2008. To Cope with Earth Environmental Changes Long Term Ecological Research. Korea Forest Research Institute research report (in Korean).
  13. Korea Rural Economic Institute. 2009. Agricultural Outlook 2009. Korea Rural Economic Institute (in Korean).
  14. Kullman, L. 2001. 20th century climate warming and tree-limit rise in the southern scandes of Sweden. Journal of the Human Environment 30(2):72-80.
  15. Lee, C.S. 2010. Vegetation change on climate change in Hanla Mt., National Institute of Environmental Research (in Korean).
  16. Lim, Y.K., D.W. Shin, S. Cocke, T.E. La Row, J.T. J.J. Schoof, O'rien, and E.P. Chassignet. 2007. Dynamically and statistically downscaled seasonal simulation of maximum surface air temperature over the southeastern United States. Journal Geophysical Research 112:D24102. doi: 10.1029/2007JD008764.
  17. Lim, Y.K. and K.Y. Kim. 2006. A new perspective on the climate prediction of Asian summer monsoon precipitation. Journal of Climate 19(19):4840-853. https://doi.org/10.1175/JCLI3905.1
  18. Nalder, I.A., and R.W. Wein. 1998. Spatial interpolation of climatic normal: test of a new method in the canadian boreal forest. Agricultural and Forest Meteorology 92(4):211-225. https://doi.org/10.1016/S0168-1923(98)00102-6
  19. Suh, M.S., J.R. Lee, J.H. Kang, D.K. Lee, and M.H. Ahn. 2005. On the relationship between seasonal change of vegetation and climate elements in East Asia, Asia-Pacific Journal of Atmospheric Sciences 41(4):557-570.

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