Incorporating non-stomatal limitation improves the performance of leaf and canopy models at high vapour pressure deficit

Abstract

Vapour pressure deficit (D) is projected to increase in the future as temperature rises. In response to increased D, stomatal conductance (gs) and photosynthesis (A) are reduced, which may result in significant reductions in terrestrial carbon, water and energy fluxes. It is thus important for gas exchange models to capture the observed responses of gs and A with increasing D. We tested a series of coupled A-gs models against leaf gas exchange measurements from the Cumberland Plain Woodland (Australia), where D regularly exceeds 2 kPa and can reach 8 kPa in summer. Two commonly used A-gs models were not able to capture the observed decrease in A and gs with increasing D at the leaf scale. To explain this decrease in A and gs, two alternative hypotheses were tested: hydraulic limitation (i.e., plants reduce gs and/or A due to insufficient water supply) and non-stomatal limitation (i.e., downregulation of photosynthetic capacity). We found that the model that incorporated a non-stomatal limitation captured the observations with high fidelity and required the fewest number of parameters. Whilst the model incorporating hydraulic limitation captured the observed A and gs, it did so via a physical mechanism that is incorrect. We then incorporated a non-stomatal limitation into the stand model, MAESPA, to examine its impact on canopy transpiration and gross primary production. Accounting for a non-stomatal limitation reduced the predicted transpiration by ~19%, improving the correspondence with sap flow measurements, and gross primary production by ~14%. Given the projected global increases in D associated with future warming, these findings suggest that models may need to incorporate non-stomatal limitation to accurately simulate A and gs in the future with high D. Further data on non-stomatal limitation at high D should be a priority, in order to determine the generality of our results and develop a widely applicable model. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.