title: A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability.
abstract: The soil CO2 emission is recognized as one of the largest fluxes in the global carbon cycle. Small errors in its estimation can result in large uncertainties and have important consequences for climate model predictions. Monte Carlo approach is efficient for estimating and reducing spatial scale sampling errors. However, that has not been used in soil CO2 emission studies. Here, soil respiration data from 51 PVC collars were measured within farmland cultivated by maize covering 25 km(2) during the growing season. Based on Monte Carlo approach, optimal sample sizes of soil temperature, soil moisture, and soil CO2 emission were determined. And models of soil respiration can be effectively assessed: Soil temperature model is the most effective model to increasing accuracy among three models. The study demonstrated that Monte Carlo approach may improve soil respiration accuracy with limited sample size. That will be valuable for reducing uncertainties of global carbon cycle.
authors.affiliation: State Key Laboratory of Loess and Quaternary Geology Institute of Earth Environment Chinese Academy of Sciences Xi'an Shaanxi 710061 China ; State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau Institute of Soil and Water Conservation Northwest A&F University Yangling Shaanxi 712100 China.
authors.forename: Wei-Yu
authors.initials: WY
authors.lastname: Shi
authors.affiliation: School of Science Xi'an University of Technology Xi'an Shaanxi 710054 China.
authors.forename: Li-Jun
authors.initials: LJ
authors.lastname: Su
authors.affiliation: State Key Laboratory of Loess and Quaternary Geology Institute of Earth Environment Chinese Academy of Sciences Xi'an Shaanxi 710061 China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Song
authors.affiliation: Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences Lanzhou 730000 China.
authors.forename: Ming-Guo
authors.initials: MG
authors.lastname: Ma
authors.affiliation: State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau Institute of Soil and Water Conservation Northwest A&F University Yangling Shaanxi 712100 China.
authors.forename: Sheng
authors.initials: S
authors.lastname: Du
keywords: Maize; Monte Carlo approach; oasis; soil respiration; uncertainty