干旱区生态环境知识资源中心(Arid): Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

Arid

DOI	10.1029/2019GB006264
	Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale
	Warner, D. L.1; Bond-Lamberty, B.2; Jian, J.2; Stell, E.3; Vargas, R.4
通讯作者	Vargas, R.
来源期刊	GLOBAL BIOGEOCHEMICAL CYCLES
ISSN	0886-6236
EISSN	1944-9224
出版年	2019
卷号	33 期号:12 页码:1733-1745
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.
英文关键词	Machine learning soil respiration soil CO2 efflux global carbon cycle
类型	Article
语种	英语
国家	USA
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000509092500015
WOS关键词	CARBON-DIOXIDE ; HETEROTROPHIC RESPIRATION ; SEMIARID ECOSYSTEMS ; WATER CONTENT ; CO2 EFFLUX ; FOREST ; TEMPERATURE ; VEGETATION ; PATTERNS ; CLIMATE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences
EI主题词	2019-12-01
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/311150
作者单位	1.Univ Delaware, Delaware Geol Survey, Newark, DE 19716 USA; 2.Pacific Northwest Natl Lab, Joint Global Change Res Inst, Richland, WA 99352 USA; 3.Univ Delaware, Dept Geog, Newark, DE USA; 4.Univ Delaware, Dept Plant & Soil Sci, Newark, DE 19717 USA
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):1733-1745.
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),1733-1745.
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):1733-1745.