Modeling soil organic carbon spatial distribution for a complex terrain based on geographically weighted regression in the eastern Qinghai-Tibetan Plateau

doi:10.1016/j.catena.2019.104399

CORC > 兰州理工大学 > 兰州理工大学 > 土木工程学院

	Modeling soil organic carbon spatial distribution for a complex terrain based on geographically weighted regression in the eastern Qinghai-Tibetan Plateau
	Wang, Dong 2,3; Li, Xinxing 4; Zou, Defu 2; Wu, Tonghua 2; Xu, Haiyan 4; Hu, Guojie 2; Li, Ren 2; Ding, Yongjian 2,3,5; Zhao, Lin 1; Li, Wangping 6
刊名	CATENA
	2020-04-01
卷号	187
关键词	Geographically weighted regression Soil organic carbon density Qinghai-Tibetan Plateau Environmental variables Permafrost
ISSN号	0341-8162
DOI	10.1016/j.catena.2019.104399
英文摘要	Permafrost regions store a large amount soil organic carbon (SOC), and the decomposition of these carbon pools can release greenhouse gases and further strength climate warming. An explicit spatial distribution of SOC is one of the basic databases for Earth System Models. However, efficient approaches for obtaining the spatial distribution of SOC remain challenging, especially in mountainous areas which are characterized by complex terrains. Here, we modeled the spatial SOC distribution using the geographically weighted regression (GWR) approach in an area on the eastern part of the Qinghai-Tibetan Plateau (QTP). We analyzed multiple environmental variables and soil profile data (n = 73) to find the best prediction models for the SOC density (SOCD) for the 0-50 cm layers. The results showed that normalized difference vegetation index (NDVI), elevation, and slope gradient are the significant predictors for the SOCD. For the upper 50 cm soil layers, the SOCD ranged from 1.08 to 18.32 kgm(-2), with higher values in mountain slopes but lower values in mountain valleys and basins. The GWR model had a higher prediction accuracy in the modeling SOCD in comparison with other models such as ordinary kriging (OK) interpolation, multiple linear regression (MLR) model. Our results showed that GWR model is a useful tool for modeling of SOC distribution and potentially can be integrated into Earth system models in areas of complex terrains.
WOS研究方向	Geology ; Agriculture ; Water Resources
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000514020400012
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/155216]
专题	土木工程学院
作者单位	1.Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210000, Peoples R China; 2.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resource, State Key Lab Cryospher Sci, Cryosphere Res Stn Qinghai Tibetan Plateau, Lanzhou 730000, Gansu, Peoples R China; 3.Univ Chinese Acad Sci, 19 A Yuquan Rd, Beijing 100049, Peoples R China; 4.Lanzhou Jiaotong Univ, Sch Environm & Municipal Engn, Lanzhou 730070, Peoples R China; 5.Chinese Acad Sci, Key Lab Ecohydrol River Basin Sci, 320 West Donggang Rd, Lanzhou 730000, Peoples R China; 6.Lanzhou Univ Technol, Sch Civil Engn, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Wang, Dong,Li, Xinxing,Zou, Defu,et al. Modeling soil organic carbon spatial distribution for a complex terrain based on geographically weighted regression in the eastern Qinghai-Tibetan Plateau[J]. CATENA,2020,187.
APA	Wang, Dong.,Li, Xinxing.,Zou, Defu.,Wu, Tonghua.,Xu, Haiyan.,...&Wu, Xiaodong.(2020).Modeling soil organic carbon spatial distribution for a complex terrain based on geographically weighted regression in the eastern Qinghai-Tibetan Plateau.CATENA,187.
MLA	Wang, Dong,et al."Modeling soil organic carbon spatial distribution for a complex terrain based on geographically weighted regression in the eastern Qinghai-Tibetan Plateau".CATENA 187(2020).