气候变化领域集成服务门户(CCPortal): Benchmark estimates for aboveground litterfall data derived from ecosystem models

CCPortal

DOI	10.1088/1748-9326/ab2ee4
	Benchmark estimates for aboveground litterfall data derived from ecosystem models
	Li S.; Yuan W.; Ciais P.; Viovy N.; Ito A.; Jia B.; Zhu D.
发表日期	2019
ISSN	17489318
卷号	14 期号:8
英文摘要	Litter production is a fundamental ecosystem process, which plays an important role in regulating terrestrial carbon and nitrogen cycles. However, there are substantial differences in the litter production simulations among ecosystem models, and a global benchmarking evaluation to measure the performance of these models is still lacking. In this study, we generated a global dataset of aboveground litterfall production (i.e. cLitter), a benchmark as the defined reference to test model performance, by combining systematic measurements taken from a substantial number of surveys (1079 sites) with a machine learning technique (i.e. random forest, RF). Our study demonstrated that the RF model is an effective tool for upscaling local litterfall production observations to the global scale. On average, the model predicted 23.15 Pg C yr-1 of aboveground litterfall production. Our results revealed substantial differences in the aboveground litterfall production simulations among the five investigated ecosystem models. Compared to the reference data at the global scale, most of models could reproduce the spatial patterns of aboveground litterfall production, but the magnitude of simulations differed substantially from the reference data. Overall, ORCHIDEE-MICT performed the best among the five investigated ecosystem models. © 2019 The Author(s). Published by IOP Publishing Ltd.
英文关键词	aboveground litterfall production; ecosystem model; leaf area index; random forest
语种	英语
scopus关键词	Benchmarking; Decision trees; Ecosystems; Statistical tests; Carbon and nitrogen; Ecosystem model; Global benchmarking; Leaf Area Index; Litterfall production; Machine learning techniques; Production simulation; Random forests; Learning systems; aboveground production; benchmarking; ecosystem modeling; litterfall; machine learning; nitrogen cycle; upscaling
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/154455
作者单位	School of Atmospheric Sciences, Zhuhai Key Laboratory of Dynamics Urban Climate and Ecology, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China; Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, Guangdong, 519082, China; Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CEA, CNRS UVSQ, Gif-sur-Yvette, 91191, France; National Institute for Environmental Studies, Tsukuba, Ibaraki, 305-8506, Japan; State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
推荐引用方式 GB/T 7714	Li S.,Yuan W.,Ciais P.,et al. Benchmark estimates for aboveground litterfall data derived from ecosystem models[J],2019,14(8).
APA	Li S..,Yuan W..,Ciais P..,Viovy N..,Ito A..,...&Zhu D..(2019).Benchmark estimates for aboveground litterfall data derived from ecosystem models.Environmental Research Letters,14(8).
MLA	Li S.,et al."Benchmark estimates for aboveground litterfall data derived from ecosystem models".Environmental Research Letters 14.8(2019).