Inherent Water-Use Efficiency of Different Forest Ecosystems and Its Relations to Climatic Variables

Abstract

Inherent water-use efficiency (IWUE) is a vital parameter connecting the carbon and water cycles. However, the factors influencing the IWUE in different forest ecosystems are still a subject of debate. In this work, FLUXNET platform measurements of 67 forest sites were used to detect trends of the IWUE of four forest ecosystems, namely deciduous broadleaf forests (DBF), evergreen broadleaf forests (EBF), needle-leaf forests (ENF), and mixed forests (MF). The IWUE differed significantly among different forest ecosystems and positively correlated with temperature and solar radiation. The IWUE of EBF was the highest at 32.02 g·C·Kg·H2O−1. The values of DBF and MF were similar and higher than that of ENF. With increasing latitude, the IWUE increased first and then decreased, with a maximum of 35° N. The IWUE of EBF was negatively correlated with precipitation and leaf area index. Temperature and solar radiation were the main factors controlling the IWUE of forest ecosystems, whereas precipitation was the major factor controlling the inter-annual variation in the ΔIWUE of forest ecosystems. Our results provide a scientific basis for the study of forest carbon sinks, forest eco-hydrological processes, and forest ecosystem responses to global climatic changes.

Comparison of carbon and water fluxes and the drivers of ecosystem water use efficiency in a temperate rainforest and a peatland in southern South America

“…The ecosystems of southern South America, where incoming weather fronts originate directly over the southern Pacific Ocean, have not been exposed to industrial pollution, in contrast to the forests of the Northern Hemisphere, so the nitrogen cycle in the study area has been defined as unpolluted (Hedin et al, 1995). Annual values of WUE showed a wide variation in the forest (between 1.94 and 3.29 g C kg −1 H 2 O) compared to the interannual variability reported for an oldgrowth subtropical forest over 7 years (between 1.70 and 1.98 g C kg −1 H 2 O; Liu et al, 2017), which may be explained by the occurrence of an extremely dry year in our study site in 2016 (Garreaud, 2018), which was associated with the highest WUE value, followed by a wet year in 2017 that was associated with the lowest WUE value. Based on measurements of NEE the forest was a net sink, while the peatland was a small source, which is consistent with an estimation made by Perez-Quezada et al (2021b), who showed that fire in this area has strong and long-term effects on the forest greenhouse gas balance, by converting the forest into an anthropogenic peatland.…”

“…These three variables were also identified as the main drivers of ecosystem carbon and water fluxes for northern peatlands in Canada (Humphreys et al, 2006). Liu et al (2017) also found global radiation and vapour pressure deficit to be among the main drivers of GPP and ET in a subtropical forest in China, although they reported that precipitation and soil moisture were even more important.…”

Dynamics of Water Use Efficiency of Coniferous and Broad-Leaved Mixed Forest in East China

“…The flux system is installed at a height of 40 m on the flux tower and equipped with primary sensors, including a three-dimensional ultrasound and CO 2 /H 2 O analyzer (IRGASON, Campbell Scientific Inc., Logan, UT, USA), a four-component radiation meter (CNR4, Kipp & Zonen, Delft, Netherlands), an air temperature and humidity sensor (HMP155A, Vaisala, Helsinki, Finland), and a soil heat flux plate (HFP01, Hukseflux, Delft, Netherlands). The six-layer gradient system is capable of measuring meteorological variables at different spatial heights (2,8,16,24,32, and 40 m) and soil depths (10,20,30,40,60, and 90 cm), utilizing sensors such as soil temperature sensors (TCAV, Campbell Scientific Inc., Logan, UT, USA), soil moisture sensors (CS616, Campbell Scientific Inc., Logan, UT, USA), wind direction sensors (020C, Met One Instruments Inc., OR, USA), wind speed sensors (010C, Met One Instruments Inc., OR, USA), a rain gauge (RG3-M, Oneset, MA, USA), and air temperature and humidity sensors (HMP155A, Campbell Scientific Inc., Logan, UT, USA). The profiling system, primarily composed of Atmospheric Profile 200 (AP200, Campbell Scientific Inc., Logan, UT, USA) and air intake components, measures the concentrations of CO 2 and H 2 O in the air at six observation levels (spatial heights of 2, 8, 16, 24, 32, and 40 m).…”

“…In forest ecosystems, the variation in WUE is particularly complex, as it is influenced by numerous interacting ecological and environmental factors [4]. Based on our current knowledge, among different types of forest ecosystems, the WUE of evergreen coniferous forests is the highest, followed by deciduous coniferous forests, evergreen broad-leaved forests, and finally, deciduous broad-leaved forests [14][15][16]. As one of the important forest types, the research on WUE in mixed coniferous-broadleaf forests is relatively scarce.…”

Forest stand factors determine the rainfall pattern of crown allocation of Picea schrenkiana in the northern slope of Mount Bogda, Tianshan Range, China

“…Therefore, we should pay attention not only to the direct benefits that forests can provide in terms of wood, biochar, and the other forest products, but also to the indirect benefits of ecosystem services with respect to climate regulation, water source conservation, and flood disaster retention and reduction, all of which depend on the dynamic relationship between forest and water ( Bosch and Hewlett, 1982 ). To sum up, it is necessary to conduct in-depth research on forest eco-hydrological processes and to evaluate their environmental benefits, at whose core is the relationship between vegetation cover and runoff, to elucidate the mechanisms underpinning allocation of crown-intercepted precipitation, water retention by litter, soil water infiltration and storage, terrestrial evapotranspiration, and so on, among forest strata such as vegetation crown, ground covering layer of plants, and the soil ( Wang et al., 2001a ; Xian et al., 2014 ; Lu et al., 2015 ; Liu et al., 2022 ).…”