气候变化领域集成服务门户(CCPortal): The role of predictive model data in designing mangrove forest carbon programs

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DOI	10.1088/1748-9326/ab7e4e
	The role of predictive model data in designing mangrove forest carbon programs
	Bukoski J.J.; Elwin A.; Mackenzie R.A.; Sharma S.; Purbopuspito J.; Kopania B.; Apwong M.; Poolsiri R.; Potts M.D.
发表日期	2020
ISSN	17489318
卷号	15 期号:8
英文摘要	Estimating baseline carbon stocks is a key step in designing forest carbon programs. While field inventories are resource-demanding, advances in predictive modeling are now providing globally coterminous datasets of carbon stocks at high spatial resolutions that may meet this data need. However, it remains unknown how well baseline carbon stock estimates derived from model data compare against conventional estimation approaches such as field inventories. Furthermore, it is unclear whether site-level management actions can be designed using predictive model data in place of field measurements. We examined these issues for the case of mangroves, which are among the most carbon dense ecosystems globally and are popular candidates for forest carbon programs. We compared baseline carbon stock estimates derived from predictive model outputs against estimates produced using the Intergovernmental Panel on Climate Change's (IPCC) three-tier methodological guidelines. We found that the predictive model estimates out-performed the IPCC's Tier 1 estimation approaches but were significantly different from estimates based on field inventories. Our findings help inform the use of predictive model data for designing mangrove forest policy and management actions. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	blue carbon; carbon accounting; carbon offsets; climate change mitigation; wetlands
语种	英语
scopus关键词	Carbon; Climate change; Climate models; Forestry; Estimation approaches; Field inventories; Field measurement; High spatial resolution; Intergovernmental panel on climate changes; Level management; Methodological guidelines; Predictive modeling; Predictive analytics; carbon cycle; carbon sequestration; data set; guideline; Intergovernmental Panel on Climate Change; mangrove; policy making; spatial resolution; Rhizophoraceae
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153912
作者单位	Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, United States; Department of Geography and Environmental Science, University of Reading, Reading, United Kingdom; Institute of Pacific Islands Forestry, United States Forest Service, Hilo, HI, United States; Institute of Ocean and Earth Sciences, University of Malaya, Kuala Lumpur, Malaysia; Department of Natural Resources and Environmental Management, University of Hawaii at Manoa, Honolulu, HI, United States; Soil Science Department, Faculty of Agriculture, Sam Ratulangi University, Manado, Indonesia; Department of Silviculture, Faculty of Forestry, Kasetsart University, Bangkok, Thailand
推荐引用方式 GB/T 7714	Bukoski J.J.,Elwin A.,Mackenzie R.A.,et al. The role of predictive model data in designing mangrove forest carbon programs[J],2020,15(8).
APA	Bukoski J.J..,Elwin A..,Mackenzie R.A..,Sharma S..,Purbopuspito J..,...&Potts M.D..(2020).The role of predictive model data in designing mangrove forest carbon programs.Environmental Research Letters,15(8).
MLA	Bukoski J.J.,et al."The role of predictive model data in designing mangrove forest carbon programs".Environmental Research Letters 15.8(2020).