干旱区生态环境知识资源中心(Arid): Soil moisture observations and machine learning reveal preferential flow mechanisms in the Qilian Mountains

Arid

DOI	10.1016/j.geoderma.2023.116626
	Soil moisture observations and machine learning reveal preferential flow mechanisms in the Qilian Mountains
	Kang, Weiming; Tian, Jie; Bogena, Heye Reemt; Lai, Yao; Xue, Dongxiang; He, Chansheng
通讯作者	Tian, J
来源期刊	GEODERMA
ISSN	0016-7061
EISSN	1872-6259
出版年	2023
卷号	438
英文摘要	The complexity of the spatial distribution and temporal occurrence of preferential flow (PF) makes it challenging to understand the mechanisms of PF. This study aims to identify the spatial and temporal patterns of PF occurrence using machine learning (Classification and Regression Trees and Random Forests) in the Qilian Mountains, Northwest China. Our results show that detected PF events transport much more rainfall down to the subsoil than non-PF events. Different vegetation types exhibit variations in the main soil layers where PF occurs, which is closely related to the distribution of roots. The PF proportion varies significantly both vertically and horizontally. Based on the Random Forests, we found that the spatial distribution of the PF proportion is mainly controlled by the saturated hydraulic conductivity and residual soil moisture, which cannot be identified by conventional correlation analysis methods. With these soil properties, the spatial distribution of the PF proportion can be estimated with reasonable performance. Using the Classification and Regression Trees method, we identified the temporal occurrence pattern of the PF for different vegetation types and all observation stations. Results indicate that the dominant factors controlling the temporal occurrence of the PF varied for different vegetation types. The thresholds at which these factors initiate the PF also varied. Finally, we found that the PF occurs particularly under wet conditions (except for hydrophobic soils), under denser vegetation, and under conditions of high rainfall amount and intensity, regardless of vegetation type. Our study confirms that both site factors (e.g., soil properties and vegetation) and temporal factors (e.g., initial soil moisture and rainfall characteristics) control the occurrence of the PF in mountainous regions such as the Qilian Mountains and that the Classification and Regression Trees has great potential to study the temporal occurrence of the PF.
英文关键词	Preferential Flow Machine learning Occurrence pattern Mountainous areas
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001064678500001
WOS关键词	DIFFERENT LAND COVERS ; DESERT-OASIS ECOTONE ; HYDRAULIC CONDUCTIVITY ; REGRESSION TREES ; MACROPORE FLOW ; WATER ; CLASSIFICATION ; SUSCEPTIBILITY ; VEGETATION ; PRECIPITATION
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396700
推荐引用方式 GB/T 7714	Kang, Weiming,Tian, Jie,Bogena, Heye Reemt,et al. Soil moisture observations and machine learning reveal preferential flow mechanisms in the Qilian Mountains[J],2023,438.
APA	Kang, Weiming,Tian, Jie,Bogena, Heye Reemt,Lai, Yao,Xue, Dongxiang,&He, Chansheng.(2023).Soil moisture observations and machine learning reveal preferential flow mechanisms in the Qilian Mountains.GEODERMA,438.
MLA	Kang, Weiming,et al."Soil moisture observations and machine learning reveal preferential flow mechanisms in the Qilian Mountains".GEODERMA 438(2023).