Knowledge Resource Center for Ecological Environment in Arid Area
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
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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. |
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