scholarly journals The Variation in Water Consumption by Transpiration of Qinghai Spruce among Canopy Layers in the Qilian Mountains, Northwestern China

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 845
Author(s):  
Yanfang Wan ◽  
Pengtao Yu ◽  
Yanhui Wang ◽  
Bin Wang ◽  
Yipeng Yu ◽  
...  

It is important for integrated forest-water management to develop a better understanding of the variation of tree transpiration among different canopy layers in the forests and its response to soil moisture and weather conditions. The results will provide insights into water consumption by trees occupying different social positions of the forests. In the present study, an experiment was conducted in the Qilian Mountains, northwest China, and 13 trees, i.e., 4–5 trees from each one of dominant (the relative tree height (HR) > 1.65), subdominant (1.25 < HR ≤ 1.65) and intermediate-suppressed (HR ≤ 1.25) layers) were chosen as sample trees in a pure Qinghai spruce (Picea crassifolia Kom.) forest stand. The sap flux density of sample trees, soil moisture of main root zone (0 to 60 cm) and meteorological conditions in open field were observed simultaneously from July to October of 2015 and 2016. The results showed that (1) The mean daily stand transpiration for the study period in 2015 and 2016 (July–October), was 0.408 and 0.313 mm·day−1, and the cumulative stand transpiration was 54.84 and 40.97 mm, accounting for 24.14% (227.2 mm) and 16.39% (249.9 mm) of the total precipitation over the same periods, respectively. (2) The transpiration varied greatly among canopy layers, and the transpiration of the dominant and codominant layers was the main contributors to the stand transpiration, contributing 86.05% and 81.28% of the stand transpiration, respectively, in 2015 and 2016. (3) The stand transpiration was strongly affected by potential evapotranspiration (PET) and volumetric soil moisture (VSM). However, the transpiration of trees from the dominant and codominant layers was more sensitive to PET changes and that from the intermediate-suppressed layer was more susceptible to soil drought. This implied that in dry period, such as in drought events, the dominant and codominant trees would transpire more water, while the intermediate-suppressed trees almost stopped transpiration. These remind us that the canopy structure was the essential factor affecting single-tree and forest transpiration in the dryland areas.

2017 ◽  
Vol 44 ◽  
pp. 76-83 ◽  
Author(s):  
Quanyan Tian ◽  
Zhibin He ◽  
Shengchun Xiao ◽  
Xiaomei Peng ◽  
Aijun Ding ◽  
...  

2012 ◽  
Vol 32 (4) ◽  
pp. 1066-1076 ◽  
Author(s):  
田风霞 TIAN Fengxia ◽  
赵传燕 ZHAO Chuanyan ◽  
冯兆东 FENG Zhaodong ◽  
彭守璋 PENG Shouzhang ◽  
彭焕华 PENG Huanhua

2017 ◽  
Vol 37 (8) ◽  
Author(s):  
王彬 WANG Bin ◽  
于澎涛 YU Pengtao ◽  
王顺利 WANG Shunli ◽  
王彦辉 WANG Yanhui ◽  
张雷 ZHANG Lei ◽  
...  

2021 ◽  
Vol 25 (6) ◽  
pp. 3455-3469
Author(s):  
Tingting Ning ◽  
Zhi Li ◽  
Qi Feng ◽  
Zongxing Li ◽  
Yanyan Qin

Abstract. Previous studies have successfully applied variance decomposition frameworks based on the Budyko equations to determine the relative contribution of variability in precipitation, potential evapotranspiration (E0), and total water storage changes (ΔS) to evapotranspiration variance (σET2) on different timescales; however, the effects of snowmelt (Qm) and vegetation (M) changes have not been incorporated into this framework in snow-dependent basins. Taking the arid alpine basins in the Qilian Mountains in northwest China as the study area, we extended the Budyko framework to decompose the growing season σET2 into the temporal variance and covariance of rainfall (R), E0, ΔS,Qm, and M. The results indicate that the incorporation of Qm could improve the performance of the Budyko framework on a monthly scale; σET2 was primarily controlled by the R variance with a mean contribution of 63 %, followed by the coupled R and M (24.3 %) and then the coupled R and E0 (14.1 %). The effects of M variance or Qm variance cannot be ignored because they contribute 4.3 % and 1.8 % of σET2, respectively. By contrast, the interaction of some coupled factors adversely affected σET2, and the out-of-phase seasonality between R and Qm had the largest effect (−7.6 %). Our methodology and these findings are helpful for quantitatively assessing and understanding hydrological responses to climate and vegetation changes in snow-dependent regions on a finer timescale.


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