干旱区生态环境知识资源中心(Arid): The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back-propagation neural network model

Arid

DOI	10.1002/hyp.14490
	The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back-propagation neural network model
	Li, Yuanhang; Chen, Qi; He, Kangning; Wang, Zuoxiao
通讯作者	He, KN
来源期刊	HYDROLOGICAL PROCESSES
ISSN	0885-6087
EISSN	1099-1085
出版年	2022
卷号	36 期号:2
英文摘要	Tree transpiration is an important water movement process in forest ecosystems, and it plays a decisive role in the coupling feedback of hydrological and ecological processes. Therefore, identifying the impact of different factors on sap flow can promote efficient water management and improve assessment of the climate change impacts. However, the interaction between sap flow and control factors is not clear, and there is no accurate model to predict sap flow change of Picea crassifolia Kom. This study explored the correlation between sap flow and environmental factors and compared the performances of the back-propagation neural network (BPANN) and multiple regression (MLR) models, and the importance of the time-lag effect and different soil moisture conditions in the response mechanism of sap flow was also explained. A higher fitting performance was found in the BPANN model (R-2 > 0.90) based on the relationships between air temperature (Ta), air relative humidity (RH), solar radiation (Rn) and vapour pressure deficit (VPD) and sap flux density (Q) than that MLR (R-2 = 0.8915). The correlation was improved due to the consideration of time-lag effect. Other variables, such as maximum temperature (Tmax), wind speed (WS) and precipitation (P), explained smaller portions of the variance in sap flow, while minimum temperature (Tmin) and leaf area index (LAI) had almost no effect. Moreover, the R-2 of the water stress condition (REW <0.38) was lower than that of the non-water stress condition (REW >= 0.38), and even lower than the R-2 of the whole experimental period. Therefore, the sap flow prediction model based on BPANN could more reasonably explain the nonlinear relationship between transpiration and control factors, which provided a basis for the estimation of plant water-use and the construction and management of ecological vegetation in alpine arid and semi-arid areas, especially in response to the continuous enhancement of aridification and climate warming.
英文关键词	driving factors Picea crassifolia Kom sap flow density the back-propagation neural network time-lag effect water stress and non-water stress conditions
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000760417000034
WOS关键词	SOIL-MOISTURE ; METEOROLOGICAL FACTORS ; WATER FLUXES ; TRANSPIRATION ; TREES ; CATCHMENT ; REGION ; PLANTATIONS ; RESPONSES ; DESERT
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393044
推荐引用方式 GB/T 7714	Li, Yuanhang,Chen, Qi,He, Kangning,et al. The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back-propagation neural network model[J],2022,36(2).
APA	Li, Yuanhang,Chen, Qi,He, Kangning,&Wang, Zuoxiao.(2022).The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back-propagation neural network model.HYDROLOGICAL PROCESSES,36(2).
MLA	Li, Yuanhang,et al."The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back-propagation neural network model".HYDROLOGICAL PROCESSES 36.2(2022).