Pedogenic knowledge-aided modelling of soil inorganic carbon stocks in an alpine environment
Yang, RM; Yang, F; Yang, F; Huang, LM; Liu, F; Yang, JL; Zhao, YG; Li, DC; Zhang, GL
2017
Source PublicationSCIENCE OF THE TOTAL ENVIRONMENT
ISSN0048-9697
Volume599Pages:1445-1453
AbstractAccurate estimation of soil carbon is essential for accounting carbon cycling on the background of global environment change. However, previous studies made little contribution to the patterns and stocks of soil inorganic carbon (SIC) in large scales. In this study, we defined the structure of the soil depth function to fit vertical distribution of SIC based on pedogenic knowledge across various landscapes. Soil depth functions were constructed from a dataset of 99 soil profiles in the alpine area of the northeastern Tibetan Plateau. The parameters of depth functions were mapped from environmental covariates using random forest. Finally, SIC stocks at three depth intervals in the upper 1 m depth were mapped across the entire study area by applying predicted soil depth functions at each location. The results showed that the soil depth functions were able to improve accuracy for fitting the vertical distribution of the SIC content, with a mean determination coefficient of R-2 = 0.93. Overall accuracy for predicted SIC stocks was assessed on training samples. High Lin's concordance correlation coefficient values (0.84-0.86) indicate that predicted and observed values were in good agreement (RMSE: 1.52-1.67 kg m(-2) and ME: 033 to 029 kg m(-2)). Variable importance showed that geographic position predictors (longitude, latitude) were key factors predicting the distribution of SIC. Terrain covariates were important variables influencing the three-dimensional distribution of SIC in mountain areas. By applying the proposed approach, the total SIC stock in this area is estimated at 75.41 Tg in the upper 30 cm, 113.15 Tg in the upper 50 cm and 19030 Tg in the upper 1 m. We concluded that the methodology would be applicable for further prediction of SIC stocks in the Tibetan Plateau or other similar areas. (C) 2017 Elsevier B.V. All rights reserved.
SubtypeJournal
KeywordPedogenic Digital soil mapping Soil inorganic carbon Random forest Exponential function Vertical pattern
Subject AreaEnvironmental Sciences & Ecology
WOS Subject ExtendedEnvironmental Sciences
WOS KeywordRANDOM FORESTS ; CLIMATE-CHANGE ; STORAGE ; GRASSLANDS ; LANDSCAPE ; TOPSOIL ; PATTERN ; CHINA
Indexed BySCI
Language英语
WOS IDWOS:000405253500040
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/43971
Collection生态系统网络观测与模拟院重点实验室_生态网络实验室
Recommended Citation
GB/T 7714
Yang, RM,Yang, F,Yang, F,et al. Pedogenic knowledge-aided modelling of soil inorganic carbon stocks in an alpine environment[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2017,599:1445-1453.
APA Yang, RM.,Yang, F.,Yang, F.,Huang, LM.,Liu, F.,...&Zhang, GL.(2017).Pedogenic knowledge-aided modelling of soil inorganic carbon stocks in an alpine environment.SCIENCE OF THE TOTAL ENVIRONMENT,599,1445-1453.
MLA Yang, RM,et al."Pedogenic knowledge-aided modelling of soil inorganic carbon stocks in an alpine environment".SCIENCE OF THE TOTAL ENVIRONMENT 599(2017):1445-1453.
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