DOI: 10.1029/2019GB006264
论文题名: Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale
作者: Warner D.L. ; Bond-Lamberty B. ; Jian J. ; Stell E. ; Vargas R.
刊名: Global Biogeochemical Cycles
ISSN: 0886-6236
EISSN: 1944-9224
出版年: 2019
卷: 33, 期: 12 语种: 英语
英文关键词: 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
学科: carbon cycle
; global
; Machine learning
; soil CO2 efflux
; soil respiration
中文摘要: 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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160075
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
Warner D.L.,Bond-Lamberty B.,Jian J.,et al. Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale[J]. Global Biogeochemical Cycles,2019-01-01,33(12)