Journal of the Korean Society of Environmental Restoration Technology (한국환경복원기술학회지)

The Korea Society of Environmental Restoration Technology (한국환경복원기술학회)
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Long-term Effects on Forest Biomass under Climate Change Scenarios Using LANDIS-II - A case study on Yoengdong-gun in Chungcheongbuk-do, Korea -

산림경관천이모델(LANDIS-II)를 이용한 기후변화 시나리오에 따른 산림의 생물량 장기변화 추정 연구 -충청북도 영동군 학산면 봉소리 일대 산림을 중심으로 -

  • Choi, Young-Eun (Dept. of Environment & Forest Resources, Chungnam National University) ;
  • Choi, Jae-Yong (Dept. of Environment & Forest Resources, Chungnam National University) ;
  • Kim, Whee-Moon (Dept. of Green & Landscape Architecture, Dankook University) ;
  • Kim, Seoung-Yeal (Dept. of Green & Landscape Architecture, Dankook University) ;
  • Song, Won-Kyong (Dept. of Green & Landscape Architecture, Dankook University)
  • 최영은 (충남대학교 산림자원학과) ;
  • 최재용 (충남대학교 산림자원학과) ;
  • 김휘문 (단국대학교 녹지조경학과) ;
  • 김성열 (단국대학교 녹지조경학과) ;
  • 송원경 (단국대학교 녹지조경학과)
  • Received : 2019.09.06
  • Accepted : 2019.10.17
  • Published : 2019.10.31
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Abstract

This study applied the LANDIS-II model to the forest vegetation of the study area in Yeongdong-gun, Korea to identify climate effects on ecosystems of forest vegetation. The main purpose of the study is to examine the long-term changes in forest aboveground biomass(AGB) under three different climate change scenarios; The baseline climate scenario is to maintain the current climate condition; the RCP 4.5 scenario is a stabilization scenario to employ of technologies and strategies for reducing greenhouse gas emissions; the RCP 8.5 scenario is increasing greenhouse gas emissions over time representative with 936ppm of $CO_2$ concentration by 2100. The vegetation survey and tree-ring analysis were conducted to work out the initial vegetation maps and data for operation of the LANDIS model. Six types of forest vegetation communities were found including Quercus mongolica - Pinus densiflora community, Quercus mongolica community, Pinus densiflora community, Quercus variabilis-Quercus acutissima community, Larix leptolepis afforestation and Pinus koraiensis afforestation. As for changes in total AGB under three climate change scenarios, it was found that RCP 4.5 scenario featured the highest rate of increase in AGB whereas RCP 8.5 scenario yielded the lowest rate of increase. These results suggest that moderately elevated temperatures and $CO_2$ concentrations helped the biomass flourish as photosynthesis and water use efficiency increased, but huge increase in temperature ($above+4.0^{\circ}C$) has resulted in the increased respiration with increasing temperature. Consequently, Species productivity(Biomass) of trees decrease as the temperature is elevated drastically. It has been confirmed that the dominant species in all scenarios was Quercus mongolica. Like the trends shown in the changes of total AGB, it revealed the biggest increase in the AGB of Quercus mongolica under the RCP 4.5 scenario. AGB of Quercus mongolica and Quercus variabilis decreased in the RCP 4.5 and RCP 8.5 scenarios after 2050 but have much higher growth rates of the AGB starting from 2050 under the baseline scenario. Under all scenarios, the AGB of coniferous species was eventually perished in 2100. In particular they were extinguished in early stages of the RCP 4.5 and RCP 8.5 scenarios. This is because of natural selection of communities by successions and the failure to adapt to climate change. The results of the study could be expected to be effectively utilized to predict changes of the forest ecosystems due to climate change and to be used as basic data for establishing strategies for adaptation climate changes and the management plans for forest vegetation restoration in ecological restoration fields.

Keywords

References

  1. Albert, M. and Schmidt, M. 2010. Climate-sensitive modelling of site-productivity relationships for Norway spruce (Picea abies (L.) Karst.) and common beech (Fagus sylvatica L.). Forest Ecology and Management, 259(4) : 739-749. https://doi.org/10.1016/j.foreco.2009.04.039
  2. Braun-Blanquet, J. 1964. Plant sociology. New York: Translated by GD Fuller and HS Conard Mc-Graw-Hill Book Co. Inc. New York.
  3. Choi JY.Lee SH. 2018. Climate Change Impact Assessment of Abies nephrolepis (Trautv.) Maxim. in Subalpine Ecosystem using Ensemble Habitat Suitability Modeling. Journal of the Korea Society of Environmental Restoration Technology. 21(1) : 103-118. (in Korean with English summary) https://doi.org/10.13087/kosert.2018.21.1.103
  4. De Bruijn, A..Gustafson, E. J..Sturtevant, B. R..Foster, J. R..Miranda, B. R..Lichti, N. I. and Jacobs, D. F. 2014. Toward more robust projections of forest landscape dynamics under novel environmental conditions: embedding PnET within LANDIS-II. Ecological Modelling. 287: 44-57. https://doi.org/10.1016/j.ecolmodel.2014.05.004
  5. ESRI. 2017. ArcGIS Desktop: Release 10.6 Redlands, CA: Environmental Systems Research Institute.
  6. Gustafson, E. J.․Miranda, B. R.․De Bruijn, A. M.․Sturtevant, B. R. and Kubiske, M. E. 2017. Do rising temperatures always increase forest productivity? Interacting effects of temperature, precipitation, cloudiness and soil texture on tree species growth and competition. Environmental modelling & software. 97 : 171-183. https://doi.org/10.1016/j.envsoft.2017.08.001
  7. Gustafson, E. J. and Miranda, B. 2018. PnET-succession v3.1 extension user guide. New York: John Wiley & Sons. pp. 65
  8. Gustafson, E. J.․Miranda, B. and Sturtevant, B. 2018. Can future CO2 concentrations mitigate the negative dffects of high Temperature and longer droughts on forest growth? Forests. 9(11): 664. https://doi.org/10.3390/f9110664
  9. He, H.S. 2008. Forest landscape models: Definitions, characterization, and classification. Forest Ecology and Management 254: 484-498. https://doi.org/10.1016/j.foreco.2007.08.022
  10. Intergovernmental Panel on Climate Change (IPCC). 2013. Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the IPCC 5th Assessment Report-Changes to the Underlying Scientific/Technical Assessment.
  11. Keane, R.E..Parsons, R. and Hessburg, P. 2002. Estimating historical range and variation of landscape patch dynamics: limitations of the simulation approach. Ecological Modelling 151: 29-49. https://doi.org/10.1016/S0304-3800(01)00470-7
  12. Kim, JH. 1992. Analysis of successional trend by transition matrix model in the mixed broadleaved - Abieds forest of Mt. Odae. Journal of Korean Society of Forest Science. 81(4):325-336. (in Korean with English summary)
  13. Kim, JH. 2003. The analysis of forest successional trend by species replacement model in the natural forest. Forest Bioenergy. 22(3):1-10. (in Korean with English summary)
  14. Kim, TG․Cho, YH and Oh, J.G. 2015. Prediction Model of Pine Forests Distribution Change according to Climate Change. Korean Journal of Ecology and Environment. 48(4) : 229-237. (in Korean with English summary) https://doi.org/10.11614/KSL.2015.48.4.229
  15. Kim, WM.Kim, SY.Song, WK.Lee, YJ.Choi YE and Choi JY. 2019. A study on the introduction of forest succession model, LANDIS-II. Proceedings of the 2019 symposium on environmental restoration technology. Seoul : Korea Society of Environmental Restoration Technology. pp. 20-30.(in Korean)
  16. Ko, DW.Joo, HS.Lee, YG and Park. CH. 2015. Review : The Current Status and Challenges of Forest. Landscape Models. Journal of Korean Forestry Society. 104(1) : 1-13. (in Korean with English summary) https://doi.org/10.14578/jkfs.2015.104.1.1
  17. Kroes Forest Research Institute(KFRI). 2015. Impact Assessment and Adaptation of Climate Changes on Forest Ecosystem. (in Korean)
  18. Larocque, G.R. 2016. Ecological Forest Management Handbook. CRC Press.
  19. Lee CB. 2003. Coloured flora of Korea. Seoul: Hyang Moon Sa. (in Korean)
  20. Lee WC. 1996. Coloured flora of Korea. Seoul: Acodemic book. (in Korean)
  21. Lee YN. 1996. Flora of Korea. Seoul: Kyo Hak Sa. (in Korean)
  22. Lim YJ. 1985. General Ecology. Seoul: Ewoo press. (in Korean)
  23. Mladenoff, D.J. and He, H.S. 1999. Design, behavior and application of LANDIS, an object-oriented model of forest landscape disturbance and succession. in Spatial modeling of forest landscape change: approaches and applications. (Mladenoff, D.J., Baker, W.L, eds). Cambridge University Press, Cambridge, UK: 125-162.
  24. Muller-Dombois, D. and H. Ellenberg. 1974. Aims and Methods of Vegetation Ecology. New York: John Wiley and Sons Inc.
  25. Nakawatase, J.M. and Peterson, D.L. 2006. Spatial variability in forest growth-climate relationships in the Olympic Mountains, Washington. Canadian Journal of Forest Research 36(1):77-91. https://doi.org/10.1139/x05-224
  26. National Institute of Meteorological Research (NIMR). 2011. Climate change scenario report 2011 to respond to the IPCC 5th Assessment Report. (in Korean)
  27. Scheller, R.M. and Mladenoff, D.J. 2004. A forest growth and biomass module for a landscape simulation model, LANDIS:design, validation, and application. Ecological Modelling 180: 211-229. https://doi.org/10.1016/j.ecolmodel.2004.01.022
  28. Shin HJ.Park GA.Park MJ and Kim SJ. 2012. Projection of Forest Vegetation Change by Applying Future Climate Change Scenario MIROC 3.2 A1B. Journal of the Korean Association of Geographic Information Studies. 15(1) : 64-85. https://doi.org/10.11108/kagis.2012.15.1.064
  29. Urban, D.L. 2005. Modeling ecological processes across scales. Ecology 86: 1996-2006. https://doi.org/10.1890/04-0918
  30. Urban, M. C. 2015. Accelerating extinction risk from climate change. Science. 348 : 571-573. https://doi.org/10.1126/science.aaa4984
  31. Werger, M. G. A. 1974. Study on concepts and techniques applied in the Zurich-Montpellier method of vegetation survey. Bothalia. 11 : 309-323. https://doi.org/10.4102/abc.v11i3.1477
  32. Yun JH.Kim JH.Oh KH and Lee BY. 2011. Distributional Change and Climate Condition of Warm-temperate Evergreen Broad-leaved Trees in Korea, Korea Journal of Environmental Ecology. 25(1) : 47-56. (in Korean with English summary)
  33. Xu, C.Gertner, G. Z and Scheller, R. M. 2009. Uncertainties in the response of a forest landscape to global climatic change. Global Change Biology. 15(1) : 116-131. https://doi.org/10.1111/j.1365-2486.2008.01705.x
  34. http://fgis.forest.go.kr/ Forest Geographical Information System(FGIS)
  35. http://www.climate.go.kr/ Korea Meteorological Administration(KMA)