One Stomatal Model to Rule Them All? Toward Improved Representation of Carbon and Water Exchange in Global Models

Abstract

Stomatal conductance schemes that optimize with respect to photosynthetic and hydraulic functions have been proposed to address biases in land‐surface model (LSM) simulations during drought. However, systematic evaluations of both optimality‐based and alternative empirical formulations for coupling carbon and water fluxes are lacking. Here, we embed 12 empirical and optimization approaches within a LSM framework. We use theoretical model experiments to explore parameter identifiability and understand how model behaviors differ in response to abiotic changes. We also evaluate the models against leaf‐level observations of gas‐exchange and hydraulic variables, from xeric to wet forest/woody species spanning a mean annual precipitation range of 361–3,286 mm yr−1. We find that models differ in how easily parameterized they are, due to: (a) poorly constrained optimality criteria (i.e., resulting in multiple solutions), (b) low influence parameters, (c) sensitivities to environmental drivers. In both the idealized experiments and compared to observations, sensitivities to variability in environmental drivers do not agree among models. Marked differences arise in sensitivities to soil moisture (soil water potential) and vapor pressure deficit. For example, stomatal closure rates at high vapor pressure deficit range between −45% and +70% of those observed. Although over half the new generation of stomatal schemes perform to a similar standard compared to observations of leaf‐gas exchange, two models do so through large biases in simulated leaf water potential (up to 11 MPa). Our results provide guidance for LSM development, by highlighting key areas in need for additional experimentation and theory, and by constraining currently viable stomatal hypotheses.

Comparing Model Representations of Physiological Limits on Transpiration at a Semi‐Arid Ponderosa Pine Site

“…This study builds upon the work of Bassiouni and Vico (2021) by implementing stomatal models within multi-layer canopy (and big-leaf) ecosystem models and solving optimization routines numerically. The findings of both studies agree; more mechanistic representations of plant hydraulic functioning did not substantially improve predictive or functional accuracy, although other studies have found contrasting results (e.g., Eller et al, 2020;Sabot et al, 2020Sabot et al, , 2022. Our results indicate that hydraulic-modified semi-empirical models, in particular MED-H, can be effectively adapted to incorporate hydraulic constraints based on measurable plant traits.…”

“…Optimization theory supports the conceptual framework of hydraulic limitation on gas exchange since the cost of hydraulic damage can be incorporated into the cost of water loss. Although there is still much discussion about how hydraulic functioning should be applied in semi‐empirical models (Lin et al., 2015), hydraulic limitations have been incorporated into semi‐empirical stomatal models (Kennedy et al., 2019; Sabot et al., 2022; Tuzet et al., 2003; Wolf et al., 2016; Xu et al., 2016; Yang et al., 2019; Zhou et al., 2013).…”

Towards species‐level forecasts of drought‐induced tree mortality risk

“…Capturing the correct stomatal sensitivity as vapour pressure deficit increases is emerging as a key knowledge gap across models, particularly at high vapour pressure deficit (Yang et al, 2019). Sabot et al (2022) demonstrated wide variability in the sensitivity of stomatal conductance to vapour pressure deficit across the latest generation of optimal stomatal models (Wolf et al, 2016;Sperry et al, 2017;Dewar et al, 2018;Eller et al, 2018). However, as the sensitivity to vapour pressure deficit emerges from the parameterisation of the hydraulic vulnerability curve and the optimisation target, this uncertainty cannot be easily constrained with observations.…”

Towards species-level forecasts of drought-induced tree mortality risk

“…Capturing the correct stomatal sensitivity as vapour pressure deficit increases is emerging as a key knowledge gap across models, particularly at high vapour pressure deficit (Yang et al, 2019). Sabot et al (2022) demonstrated wide variability in the sensitivity of stomatal conductance to vapour pressure deficit across the latest generation of optimal stomatal models (Wolf et al, 2016;Sperry et al, 2017;Dewar et al, 2018;Eller et al, 2018). However, as the sensitivity to vapour pressure deficit emerges from the parameterisation of the hydraulic vulnerability curve and the optimisation target, this uncertainty cannot be easily constrained with observations.…”