The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back‐propagation neural network model

Abstract

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 (R2 > 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 (R2 = 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 R2 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 R2 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.

The Seasonal Variability and Environmental Factors Influencing the Transpiration of Western Juniper (Juniperus occidentalis) Saplings

“…Accurate evapotranspiration estimation could be essential to explaining the terrestrial water cycle under local environmental conditions in these regions [2,3]. Tree transpiration, a large contributor to evapotranspiration, is a significant physiological and hydrological process [4,5] and the major pathway for plant water loss from forest ecosystems in dryland regions [6]. Sap flow, which represents the movement of water from roots to leaves through the stem xylem, is commonly used to investigate the response of plant transpiration to environmental variables [7].…”

A Novel Crossed Hysteresis Response Pattern of Sap Flux to Solar Radiation

“…Driven by solar radiation and atmospheric water demand, plants absorb water from the soil, transport the water through the xylem, and ultimately transpire them into the atmosphere through plants' stomata (Li et al., 2022). From such perspective, the hysteresis is supposed to be driven by the transportation time of water from soil to leaves and the corresponding response to the environmental conditions (Ma et al., 2023).…”

Assessment of the Potential of Indirect Measurement for Sap Flow Using Environmental Factors and Artificial Intelligence Approach: A Case Study of Magnolia denudata in Shanghai Urban Green Spaces

“…Initially, linear regression models were used to analyze sap flow data based on long time series as inputs for AI algorithms. However, researchers later realized that there are complex nonlinear relationships between environmental factors and sap flow [24], leading them to turn to more powerful machine learning algorithms such as support vector machines, extreme gradient boosting, random forests, and single-layer neural networks (ANN) [24][25][26][27][28][29]. However, recent research has focused on the field of deep learning, exploring more complex models.…”