Adaptation of Asia-Pacific forests to climate change

Abstract

Climate change is a threat to the stability and productivity of forest ecosystems throughout the Asia-Pacific region. The loss of forests due to climate-induced stress will have extensive adverse impacts on biodiversity and an array of ecosystem services that are essential for the maintenance of local economies and public health. Despite their importance, there is a lack of decision-support tools required to evaluate the potential effects of climate change on Asia-Pacific ecosystems and economies and to aid in the development of regionally appropriate adaptation and mitigation strategies. The project Adaptation of Asia-Pacific Forests to Climate Change, summarized herein, aims to address this lack of knowledge and tools and to provide support for regional managers to develop effective policy to increase the adaptive capacity of Asia-Pacific forest ecosystems. This objective has been achieved through the following activities: (1) development of a high-resolution climate downscaling model, ClimateAP, applicable to any location in the region; (2) development of climate niche models to evaluate how climate change might affect the distribution of suitable climatic conditions for regionally important tree species; (3) development and application of forest models to assess alternative management strategies in the context of management objectives and the projected impacts of climate change; (4) evaluation of models to assess forest fire risk and the relationship between forest fire and climate change; (5) development of a technique to assess ecosystem carbon storage using LiDAR; and (6) evaluation of how vegetation dynamics respond to climate change using remote sensing technology. All project outputs were developed with a focus on communication and extension to facilitate the dissemination of results to regional forest resource managers to support the development of effective mitigation and adaptation policy.

Keywords

Adaptation / Asia-Pacific / Climate change / Climate model / Ecological model

References

[1] Anderson RG, Canadell JG, Randerson JT, Jackson RB, Hungate BA, Baldocchi DD, et al. Biophysical considerations in forestry for climate protection. Front Ecol Environ, 2010, 9: 174-182.

[2] Asia Development Bank (ADB) The economics of climate change in Southeast Asia: a regional review. 2009, Manila: Asia Development Bank, 255.

[3] Asner GP, Powell GVN, Mascaro J, Knapp DE, Clark JK, Jacobson J, Kennedy-Bowdoin T, Balaji A, Paex-Acosta G, Victoria E, Secada L, Valqui M, Hughes RF. High-resolution forest carbon stocks and emissions in the Amazon. Proc Natl Acad Sci, 2010, 107(38): 16738-16742.

[4] Baccini A, Goetz SJ, Walker WS, Laporte NT, Sun M, Sulla-Menashe D, Hackler J, Beck PSA, Dubayah R, Friedl MA, Samanta S, Houghton RA. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat Clim Change, 2012, 2: 182-185.

[5] Beck HE, McVicar TR, van Dijk AIJM, Schellekens J, de Jeu RAM, Bruijnzeel LA. Global evaluation of four AVHRR-NDVI data sets: intercomparison and assessment against Landsat imagery. Remote Sens Environ, 2011, 44: 2547-2563.

[6] Brockhaus M, Di Gregorio M, Mardiah S. Governing the design of national REDD+: an analysis of the power of agency. For Policy Econ, 2014, 49: 23-33.

[7] Bustamante M, Robledo-Abad C, Harper R, Mbow C, Ravindranat NH, Sperling F, Haberl H, de Siqueira Pinto A, Smith P. Co-benefits, trade-offs, barriers and policies for greenhouse gas mitigation in the agriculture, forestry and other land use (AFOLU) sector. Glob Change Biol, 2014, 20: 3270-3290.

[8] Canadell JG, Raupach MR. Managing forest for climate change mitigation. Science, 2008, 320: 1456-1457.

[9] Cao L, Coops NC, Hermosilla T, Innes J, Dai J, She G. Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests. Remote Sens, 2014, 6: 7110-7135.

[10] Cao L, Coops NC, Innes J, Dai J, She G. Mapping above-and below-ground biomass components in subtropical forests using small-footprint LiDAR. Forests, 2014, 5: 1356-1373.

[11] Chang Y, Zhu ZL, Bu RC, et al. Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China. Landsc Ecol, 2013, 28(10): 1989-2004.

[12] Che M, Chen B, Innes JL, Wang G, Dou X, Zhou T, Zhang H, Yan J, Xu G, Zhao H. Spatial and temporal variation in the end date of the vegetation growing season throughout the Qinghai-Tibetan Plateau from 1982 to 2011. Agric For Meteorol, 2014, 189–190: 81-90.

[13] Chen B, Xu G, Coops NC, Ciais P, Innes JL, Wang G, Myneni RB. (2014a). Satellite-observed changes in terrestrial photosynthesis activity trends across the Asia-Pacific region associated with land cover and climate from 1982 to 2011. J Geophys Res Biogeosci (in review)

[14] Chen B, Xu G, Coops NC, Ciais P, Innes JL, Wang G, Myneni RB, Wang T, Krzyzanowski J, Li Q, Cao L, Liu Y. Changes in vegetation photosynthetic activity trends across the Asia-Pacific region over the last three decades. Remote Sens Environ, 2014, 144: 28-41.

[15] Christensen, JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon W-T, Laprise R, Magaña Rueda V, Mearns L, Menéndez CG, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

[16] Christensen JH, Krishna Kumar K, Aldrian E, An S-I, Cavalcanti IFA, de Castro M, Dong W, Goswami P, Hall A, Kanyanga JK, Kitoh A, Kossin J, Lau NC, Renwick J, Stephenson DB, Xie S-P, Zhou T (2013) Climate phenomena and their relevance for future regional climate change. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds.) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

[17] Ciais P, Reichstein M, Viovy N, Granier A, Ogee J, Allard V, Aubinet M, Buchmann N, Bernhofer C, Carrara A. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature, 2005, 437: 529-533.

[18] Coops NC, Hember RA, Waring RH. Assessing the impact of current and projected climates on Douglas-Fir productivity in British Columbia, Canada, using a process-based model (3-PG). Can J For Res, 2010, 40(3): 511-524.

[19] Critical Ecosystem Partnership Fund (CEPF) (2014) Hotspots—Asia-Pacific. Available at http://www.cepf.net/resources/hotspots/Asia-Pacific/Pages/default.aspx, Accessed March 11 2015

[20] Davis MB, Shaw RG. Range shifts and adaptive responses to quaternary climate change. Science, 2001, 292: 673-679.

[21] Flannigan M, Stocks B, Turetsky M, Wotton M. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob Change Biol, 2008, 15(3): 549-560.

[22] Food and Agriculture Organization of the United Nations (FAO) Asia-Pacific forests and forestry to 2020: report of the second Asia-Pacific forestry sector outlook study. 2010, Bangkok: Food and Agriculture Organization of the United Nations, 236.

[23] Food and Agriculture Organization of the United Nations (FAO) Climate change for forest policy-makers: an approach for integrating climate change into national forest programmes in support of sustainable forest management. 2011, Rome: Food and Agriculture Organization of the United Nations, 50.

[24] Forest Governance Learning Group (FGLG) Indonesia: supporting governance in REDD+ and community forestry. 2014, London: International Institute for Environment and Development, 36.

[25] Girvetz EH, Zganjar C, Raber GT, Maurer EP, Kareiva P, Lawler JJ. Applied climate-change analysis: the climate wizard tool. PLoS One, 2009, 4 12 e8320

[26] Guo FT, Hu GH, Jin S, Ma ZH, Zhang Y. Relationship between forest lighting fire occurrence and weather factors in Daxing’an Mountains based on negative binomial model and zero-inflated negative binomial models. Chin J Plant Ecol, 2010, 34(5): 571-577.

[27] Guo F, Innes JL, Wang G, Ma X, Sun L, Hu H, Su Z. Historic distribution and driving factors of human-caused fire in the Chinese boreal forest during 1972–2005. J Plant Ecol, 2014, 8: 480-490.

[28] Guo F, Wang G, Innes JL, Ma X, Sun L, Hu H. Gamma generalized linear model to investigate the effects of climate variables on the area burned by forest fire in northeast China. J For Res, 2015, 26(3): 545-555.

[29] Guo F, Wang G, Innes JL, Ma Z, Liu A, Lin Y. Comparison of six generalized linear models for modeling the occurrence of lightening-induced fires in northern Daxing’an Mountains, China. J For Res, 2016, 27(2): 379-388.

[30] Hanna CT. Statistical methods in hydrology, 2002 2 Ames: Iowa State University Press

[31] Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau G, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG. High-resolution global maps of 21st-century forest cover change. Science, 2013, 342: 850-853.

[32] Heltberg R, Siegel PB, Jorgensen SL. Addressing human vulnerability to climate change: towards a ‘no-regrets’ approach. Glob Environ Change, 2009, 19: 89-99.

[33] Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol, 2005, 25: 1965-1978.

[34] Innes J, Joyce LA, Kellomäki S, Louman B, Ogden A, Parrotta J, Thompson I, Ayres M, Ong C, Santoso H, Sohngen B, Wreford A. 2009. Management for adaptation. In: Seppälä R, Buck A, Katila P (eds.) Adaptation of forests and people to climate change—a global assessment report: IUFRO World Series Volume 22. International Union for Forest Research Organizations (IUFRO), Helsinki, pp. 135–186

[35] Intergovernmental Panel on Climate Change (IPCC) (2014a). Climate change 2014a. Synthesis Report. In: Core Writing Team, Pachauri RK, Meyer LA (eds.) Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, Geneva

[36] Intergovernmental Panel on Climate Change (IPCC) (2014b). Climate change 2014: the physical science basis. In: Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

[37] International Institute for Sustainable Development (IISD) (2013). What is REDD+? Available at https://www.iisd.org/climate/land_use/redd/about.aspx, Accessed March 13 2015

[38] Jackson RB, Randerson JT, Canadell JG, et al. Protecting climate with forests. Environ Res Lett, 2008, 3 044006

[39] Kang H (2015) Study on the adaptive management of Chinese fir plantations to climate change. Ph.D. Thesis, Fujian Agriculture and Forestry University (in Chinese)

[40] Kang H, Wang T, Wang G, Innes JL, Zheng D, Chen P, Seely B, Li Q (2015) A local application of climate niche models for Chinese fir and possible adaptation strategies under changing climate. PLoS One. (in review)

[41] Keenan T, Maria Serra J, Lloert F, Ninyerola M, Sabate S. Predicting the future of forests in the Mediterranean under climate change, with niche- and process-based models: CO₂ matters!. Glob Change Biol, 2011, 17: 565-579.

[42] Kimmins JP, Mailly D, Seely B. Modelling forest ecosystem net primary production: the hybrid simulation approach used in FORECAST. Ecol Model, 1999, 122: 195-224.

[43] Knutti R, Masson D, Gettelman A. Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett, 2013, 40: 1194-1199.

[44] Kou X, Li Q, Beierkuhnlein C, Zhao Y, Liu S. A new tool for exploring climate change induced range shifts of conifer species in China. PLoS One, 2014, 9 9 e98643

[45] Krzyzanowski J (2014a) Climate change and forest policy in the Asia-Pacific region. http://asiapacific.forestry.ubc.ca/research-approaches/literature-review/, Accessed March 13 2015

[46] Krzyzanowski J (2014b) Climate change and forestry in the Asia-Pacific region. Manuscript in preparation. http://asiapacific.forestry.ubc.ca/research-approaches/literature-review/, Accessed March 13 2015

[47] Kumar NBK (2012) Community forestry: well placed to address climate change challenges in the Terai, Nepal. In: Forestry: case studies in Asia. Regional Community Forestry Training Center for Asia-Pacific, Bangkok, pp. 37–46

[48] Landsberg JJ, Waring RH. A generalized model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For Ecol Manag, 1997, 95(3): 209-228.

[49] Lefsky MA, Cohen WB, Parker GG, Harding DJ. Lidar remote sensing for ecosystem studies. Bioscience, 2002, 52: 19-30.

[50] Lu Y, Coops NC, Bolton DK, Wang T, Wang G. Comparing stem volume predications of coastal Douglas-fir stand in British Columbia using a simple physiological model and LiDAR remote sensing. For Sci, 2014, 61: 586-596.

[51] Lu Y, Coops NC, Wang T, Want G. A process-based approach to estimating Chinese fir (Cunninghamia lanceolata) distribution and productivity in southern China under climate change. Forests, 2015, 6(2): 360-379.

[52] McKenny DW, Pedlar JH. Spatial models of site index based on climate and soil properties for two boreal tree species in Ontario, Canada. For Ecol Manag, 2003, 175: 497-507.

[53] Mitchell TD, Jones PD. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol, 2005, 25: 693-712.

[54] Mladenoff DJ. LANDIS and forest landscape models. Ecol Model, 2004, 180: 7-19.

[55] Nitschke CR, Innes JL. A tree and climate assessment tool for modelling ecosystem response to climate change. Ecol Model, 2008, 210: 263-277.

[56] Nitschke CR, Amoroso M, Coates KD, Astrup R. The influence of climate change, site type and disturbance on stand dynamics in northwest British Columbia, Canada. Ecosphere, 2012, 3 1 11

[57] Ogden AE, Innes JL. Application of structured decision making to an assessment of climate change vulnerabilities and adaptation options for sustainable forest management. Ecol Soc, 2009, 14(1): 11-39.

[58] Pan T, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG, et al. A large and persistent carbon sink in the world’s forests. Science, 2011, 333: 988-993.

[59] Paudel NS, Khatri DB, Khanal DR, Karki R (2013) The context of REDD+ in Nepal: drivers agents and institutions. Occasional Paper no. 81. Center for International Forestry Research (CIFOR), Bogor

[60] Pham TT, Moeliono M, Nguyen TH, Nguyen HT, Vu TH (2012) The context of REDD+ in Vietnam: drivers, agents and institutions. Occasional Paper no. 75. Center for International Forestry Research (CIFOR), Bogor

[61] Rawlings JO, Pantula SG, Dickey DA. Applied regression analysis: a research tool, 1998 2 New York: Springer

[62] Regional Community Forestry Training Center for Asia-Pacific (RECOFTC) Annual report 2011/2012. 2013, Bangkok: Regional Community Forestry Training Center for Asia-Pacific, 12.

[63] Regional Community Forestry Training Center for Asia-Pacific (RECOFTC) (2014). Forests and climate change after Warsaw: and Asia-Pacific perspective implications on the UNFCCC COP 19 on forest policy and practice. Regional Community Forestry Training Center for Asia-Pacific and Food and Agriculture Organization of the United Nations, Bangkok

[64] Scheller RM, Mladenoff DJ. A forest growth and biomass module for a landscape simulation model, LANDIS: design, validation, and application. Ecol Model, 2004, 180: 211-229.

[65] Schwalm CR, William CA, Schaeger K, Arneth A, Bonal D, Buchmann N, Chen J, Law BR, Lindroth A, Luyssaert A. Assimilation exceeds respiration sensitivity to drought: a fluxnet synthesis. Glob Change Biol, 2010, 16: 657-670.

[66] Seely B, Arp P, Kimmins JP (1997) A forest hydrology submodel for simulating the effect of management and climate change on stand water stress. In: Amaro A, Tomé M (eds.) Proceedings of empirical and Process-based models for forest, tree and stand growth simulation. Edições Salamandra, Lisboa, Oeiras, 21–27 September 1997, pp 463–477

[67] Seely B, Welham C, Scoullar K. Application of a hybrid forest growth model to evaluate climate change impacts on productivity, nutrient cycling and mortality in a montane forest ecosystem. PLoS One, 2015, 10 8 e0135034

[68] Shugart HH, Noble IR. A computer model of succession and fire response of the high altitude Eucalyptus forest of the Brindabella range, Australian Capital Territory. Aust J Ecol, 1981, 6: 149-164.

[69] Sturm M, Holmgren J, McFadden JP, Liston G, Chapin FS III, Racine CH. Snow-chrub interactions in Arctic tundra: a hypothesis with climatic implications. J Clim, 2001, 14: 336-344.

[70] Sturm M, Schimel J, Michaelson G, Welker JM, Oberbauer SF, Liston GE, Fahenstock J, Romanovsky VE. Winter biological processes could help convert Arctic tundra to shrubland. Bioscience, 2005, 55: 17-26.

[71] Tape KD, Lord R, Marshall HP, Ruess RW. Snow-mediated ptarmigan browsing and shrub expansion in Arctic Alaska. Ecoscience, 2010, 17: 186-193.

[72] Taylor KE, Stouffer RJ, Meehl GA. An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc, 2012, 93: 485-498.

[73] United Nations Collaborative Programme on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (UN-REDD) (2009) About REDD+. http://www.un-redd.org/aboutredd/tabid/102614/default.aspx, Accessed March 13 2015

[74] United Nations Environment Programme (UNEP) State of biodiversity in Asia and the Pacific. 2010, Bangkok: United Nations Environment Programme, 8.

[75] Vickers B, Kant P, Lasco R, Bleaney A, Milne S, Suzuki R, Ramos L, Pohan E (2010) Forests and climate change working paper 7: forests and climate change in the Asia-Pacific Region. Food and Agriculture Organization of the United Nations, Rome

[76] Wang T, Hamann A, Spittlehouse DL, Aitken SN. Development of scale-free climate data for western Canada for use in resource management. Int J Climatol, 2006, 26: 383-397.

[77] Wang T, Hamann A, Spittlehouse DL, Murdock TQ. Climate WNA—high-resolution spatial climate data for Western North America. J Appl Meteorol Climatol, 2012, 51: 16-29.

[78] Wang GY, Mang S, Krzyzanowski J, Guo FT, Wang TL, Riehl B, Kang HJ, Li QL, Innes JL (2015) Climate change and forest adaptation in the Asia-Pacific. J Geogr Res No. 63. Doi: 10.6234/JGR.2015.63.04

[79] Wang T, Wang G, Innes J, Nitschke G, Kang H. Climatic niche models and their consensus projections for future climates for four major forest tree species in the Asia-Pacific region. For Ecol Manag, 2016, 360: 357-366.

[80] Xu G, Zhang H, Chen B, Zhang H, Innes JL, Wang G, Yan J, Zheng Y, Myneni RB. Changes in vegetation growth dynamics and relations with climate over China’s landmass from 1982 to 2011. Remote Sens, 2014, 6: 3263-3283.

[81] Xu G, Zhang H, Chen B, Zhang H, Yan J, Chen J, Che M, Lin X, Dou X. A Bayesian based method to generate a synergetic land-cover map from existing land-cover products. Remote Sens, 2014, 6: 5589-5613.

[82] Yan J, Chen B, Feng M, Innes JL, Wang G, Fang S, Xu G, Zhang H, Fu D, Wang H, Yu G, Sun X (2013) Research on land surface thermal-hydrological exchange in southern China under future climate and land cover scenarios. Adv Meteorol 2013:1–12. doi:10.1155/2013/969145

[83] Yu C, Hu H, Wei R. Dynamic analysis of meteorological conditions of forest fire in Tahe forestry bureau of Daxing’an mountains. J Northeast For Univ, 2007, 35(8): 23-25.

[84] Zeng FW, Collatz GJ, Pinzon JE, Ivanoff A. Evaluating and quantifying the climate-driven interannual variability in global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) at global scales. Remote Sens, 2013, 5: 3918-3950.

[85] Zhang L, Liu S, Sun P, Wang T, Wang G, Zhang X, Wang L (2015) Consensus forecasting of species distributions: the effects of Niche model performance and Niche properties. PLoS One 10(3):e0120056

[86] Zhao M, Running SW. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 2010, 329: 940-943.