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A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability
Shi, Wei-Yu1,2; Su, Li-Jun3; Song, Yi1; Ma, Ming-Guo4; Du, Sheng2
2015-10-01
Source PublicationECOLOGY AND EVOLUTION
ISSN2045-7758
Volume5Issue:19Pages:4480-4491
SubtypeArticle
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 25km(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.

KeywordMaize Monte Carlo Approach Oasis Soil Respiration Uncertainty
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1002/ece3.1729
WOS KeywordSemiarid Loess Plateau ; Terrestrial Ecosystems ; Climate-change ; Respiration ; Temperature ; Efflux ; China ; Flux ; Transpiration ; Dependence
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEcology
WOS IDWOS:000362523300022
PublisherWILEY-BLACKWELL
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ieecas.cn/handle/361006/9240
Collection生态环境研究室
Corresponding AuthorShi, Wei-Yu
Affiliation1.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Shaanxi, Peoples R China
2.Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China
3.Xian Univ Technol, Sch Sci, Xian 710054, Shaanxi, Peoples R China
4.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China
Recommended Citation
GB/T 7714
Shi, Wei-Yu,Su, Li-Jun,Song, Yi,et al. A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability[J]. ECOLOGY AND EVOLUTION,2015,5(19):4480-4491.
APA Shi, Wei-Yu,Su, Li-Jun,Song, Yi,Ma, Ming-Guo,&Du, Sheng.(2015).A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability.ECOLOGY AND EVOLUTION,5(19),4480-4491.
MLA Shi, Wei-Yu,et al."A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability".ECOLOGY AND EVOLUTION 5.19(2015):4480-4491.
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