涡度相关法
蒸散量
环境科学
生长季节
潜热
显热
降水
大气科学
高原(数学)
土壤水分
焊剂(冶金)
水文学(农业)
生态系统
土壤科学
生态学
地理
地质学
化学
气象学
数学分析
有机化学
岩土工程
生物
数学
作者
Yaxuan Chang,Yongjian Ding,Shiqiang Zhang,Jia Qin,Qi Zhao
标识
DOI:10.1016/j.jhydrol.2022.128282
摘要
To understand the water, energy, and carbon cycles in the Tibetan Plateau (TP), it is essential to estimate seasonal and inter-annual variations in energy fluxes and evapotranspiration (ET) for alpine meadow ecosystems. The multiyear (2014–2019) energy fluxes and ET, for a typical alpine meadow at Arou station (northeastern TP), and their environmental and biophysical controls were evaluated using the eddy covariance method in this study. Latent heat flux (LE) was the dominant component of energy consumption during the growing season, whereas sensible heat flux (H) dominated energy partitioning during the non-growing season. H showed the opposite trend to LE, while the seasonal variation of soil heat flux (G) was small. The daily ET was primarily controlled by the available energy on the seasonal scale. Soil water content (SWC) and normalized difference vegetation index (NDVI) displayed secondary effects on ET during the non-growing and growing seasons, respectively. The inter-annual ET was relatively stable, ranging from 562.6 to 661.9 mm (coefficient of variation; CV = 7.4 %); this was slightly higher than the annual precipitation despite large variations in inter-annual precipitation (CV = 19.9 %) and was most likely due to snow and frozen ground melting. The cumulative ET in the growing season was about 77 % of the annual ET. There was a nonlinear increase in the daily Priestley–Taylor coefficient (α = ET/ETeq, where ETeq is the equilibrium evaporation) with an increase in bulk surface conductance (gc), which was insensitive to increases in gc that exceeded 15 mm s−1. There was a good relationship between gc and NDVI. This study provides insights into the driving mechanisms of long-term variations in the energy partitioning and biophysical controls on ET in alpine meadow ecosystems.
科研通智能强力驱动
Strongly Powered by AbleSci AI