生态水文学
环境科学
雨季
生态学
季节性
水文学(农业)
干旱化
蒸腾作用
大气科学
生物
生态系统
气候变化
地质学
植物
光合作用
岩土工程
作者
Guodong Jia,Magali F. Nehemy,Lixin Chen,Xinxiao Yu,Ziqiang Liu
标识
DOI:10.1016/j.jhydrol.2022.127887
摘要
The interspecific and temporal dynamics of tree water use are poorly understood. We investigated in high-temporal resolution patterns of water use of two tree species, Platycladus orientalis and Quercus variabilis, in a temperate mountainous monsoon forest in northern China. We leverage a unique sampling design where we systematically traced tree water source of ten individuals continuously throughout a three-year period (2015–2017) using stable isotopes of hydrogen and oxygen, coupled with root distribution assessment and measurements of transpiration rates. We observed species-specific patterns in tree water use. Q. variabilis withdrew water evenly from different soil layers without detectable seasonal changes in pattern in water use throughout the different years. By contrast, P. orientalis increases shallow source water uptake during the rainy season in all three years. Isotopic composition of both species plotted along an evaporation line below the local meteoric water line (LMWL). However, during sporadic periods in the rainy and transition season, P. orientalis plotted on the LMWL and showed similar isotopic to spring water. This suggests an ephemeral ecohydrological connectivity between tree water source and spring water during short periods within the wet and transition season. The observed distinct patterns in tree water use between the two species may be associated with root distribution characteristics. Besides root morphological traits, our data also suggest that the influence of overall stem volume (Vob) on water source contribution depends on the rainfall and species. Our results support the seasonality of ecohydrological separation observed in previous studies. Additionally, our data show that that ecohydrological separation may be species dependent in a temperate mountainous monsoon forest.
科研通智能强力驱动
Strongly Powered by AbleSci AI