气候变化领域集成服务门户(CCPortal): Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

CCPortal

DOI	10.1029/2019GB006264
	Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale
	Warner D.L.; Bond-Lamberty B.; Jian J.; Stell E.; Vargas R.
发表日期	2019
ISSN	0886-6236
EISSN	1944-9224
起始页码	1733
结束页码	1745
卷号	33 期号:12
英文摘要	Soil respiration (Rs), the soil-to-atmosphere CO2 flux produced by microbes and plant roots, is a critical but uncertain component of the global carbon cycle. Our current understanding of the variability and dynamics is limited by the coarse spatial resolution of existing estimates. We predicted annual Rs and associated uncertainty across the world at 1-km resolution using a quantile regression forest algorithm trained with observations from the global Soil Respiration Database spanning from 1961 to 2011. This model yielded a global annual Rs estimate of 87.9 Pg C/year with an associated global uncertainty of 18.6 (mean absolute error) and 40.4 (root mean square error) Pg C/year. The estimated annual heterotrophic respiration (Rh), derived from empirical relationships with Rs, was 49.7 Pg C/year over the same period. Predicted Rs rates and associated uncertainty varied widely across vegetation types, with the greatest predicted rates of Rs in evergreen broadleaf forests (accounting for 20.9% of global Rs). The greatest prediction uncertainties were in northern latitudes and arid to semiarid ecosystems, suggesting that these areas should be targeted in future measurement campaigns. This study provides predictions of Rs (and associated prediction uncertainty) at unprecedentedly high spatial resolution across the globe that could help constrain local-to-global process-based models. Furthermore, it provides insights into the large variability of Rs and Rh across vegetation classes and identifies regions and vegetation types with poor model performance that should be prioritized for future data collection. ©2019. American Geophysical Union. All Rights Reserved.
英文关键词	carbon cycle; global; Machine learning; soil CO2 efflux; soil respiration
语种	英语
scopus关键词	air-soil interaction; algorithm; annual variation; broad-leaved forest; carbon cycle; carbon flux; global change; global perspective; prediction; semiarid region; soil emission; soil respiration; spatial analysis; uncertainty analysis
来源期刊	Global Biogeochemical Cycles
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/129680
作者单位	Delaware Geological Survey, University of Delaware, Newark, DE, United States; Pacific Northwest National Laboratory, Joint Global Change Research InstituteMD, United States; Department of Geography, University of Delaware, Newark, DE, United States; Department of Plant and Soil Sciences, University of Delaware, Newark, DE, United States
推荐引用方式 GB/T 7714	Warner D.L.,Bond-Lamberty B.,Jian J.,et al. Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale[J],2019,33(12).
APA	Warner D.L.,Bond-Lamberty B.,Jian J.,Stell E.,&Vargas R..(2019).Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale.Global Biogeochemical Cycles,33(12).
MLA	Warner D.L.,et al."Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale".Global Biogeochemical Cycles 33.12(2019).