Canopy Height and Climate Dryness Parsimoniously Explain Spatial Variation of Unstressed Stomatal Conductance

Abstract

Hetherington & Woodward, 2003). Under reference conditions of low water stress and non-limiting radiation and temperature, the open apertures of stomata lead to unstressed stomatal conductance (g s,u ). During periods of water, light, or temperature stress, stomata close, thus downregulating stomatal conductance. Over the past decades, much attention has been focused on evaluating the reduction of stomatal conductance in response to

Ecosystem Water‐Saving Timescale Varies Spatially With Typical Drydown Length

“…This approach is known as environmental filtering (e.g., Famiglietti et al., 2023; Walker et al., 2017). Our findings are consistent with previous findings of instantaneous stomatal parameters being related to aridity (Bassiouni et al., 2023; Lin et al., 2015; Y. Liu et al., 2022) and rainfall intensity and frequency (Bassiouni et al., 2023). The use of environmental filtering in stomatal closure model parametrization may improve ET estimation, as previously shown across the globe using a simplified Shuttleworth‐Wallace model (Wu et al., 2020).…”

“…This approach is known as environmental filtering (e.g., Famiglietti et al, 2023;Walker et al, 2017). Our findings are consistent with previous findings of instantaneous stomatal parameters being related to aridity (Bassiouni et al, 2023;Lin et al, 2015;Y. Liu et al, 2022) and rainfall intensity and frequency (Bassiouni et al, 2023).…”

Global net biome CO2exchange predicted comparably well using parameter–environment relationships and plant functional types

“…The top‐down EF‐based hypotheses implemented here yielded NBE errors that matched or outperformed those from traditional PFTs at a sizable fraction of pixels (55%; Figure 4d), suggesting that the introduction of more realistic trait variability in large‐scale TBMs can help to improve predictions of its future behavior, as previously hypothesized (Matheny et al, 2017; Scheiter et al, 2013; van Bodegom et al, 2014; Xu & Trugman, 2021). Overall, our findings support the growing paradigm shift away from the representation of static PFTs and towards the incorporation of realistic trait variability into large‐scale TBMs (Berzaghi et al, 2020; Bloom et al, 2016; Jung & Hararuk, 2022; Liu et al, 2022; van Bodegom et al, 2014). EF‐based hypotheses represent one promising and flexible approach for doing so, although they are not a panacea—PFT‐based assumptions are still superior at nearly half of vegetated pixels in our analysis (45%; Figure 4d).…”

“…Overall, our findings support the growing paradigm shift away from the representation of static PFTs and towards the incorporation of realistic trait variability into large-scale TBMs (van Bodegom et al, 2014;Bloom et al, 2016;Berzaghi et al, 2020;C. G. Jung & Hararuk, 2022;Y. Liu et al, 2022).…”