A recent study found that current leaf-level empirical models over-predict stomatal conductance during drought conditions and a recently proposed model improves predictions during drought conditions.
Including the impairment of soil-to-leaf water transport will improve predictions of stomatal conductance during drought conditions. Many biomes contain a diversity of plant stomatal strategies during water stress.
Ecosystem models rely on empirical relationships to predict stomatal responses to changing environmental conditions, but these are not well tested during drought conditions. A team from the University of Utah, in conjunction with NGEE-Tropics compiled datasets of stomatal conductance and leaf water potential for 34 woody plant species that span global forest biomes. They tested how well three major stomatal models and a recently proposed model predicted measured stomatal conductance. They found that current models consistently over predicted stomatal conductance during dry conditions whereas the recently proposed model, which includes loss of hydraulic transport capacity, improved predictions compared to current models, particularly during droughts. These results also show that many biomes contain a diversity of plant stomatal strategies during water stress. Such improvements in stomatal simulation will help to predict the response of ecosystems to future climate extremes.
Contacts (BER PM)
William R. L. Anderegg
University of Utah
Funding for this research was provided by NSF DEB EF-1340270 and the Climate Mitigation Initiative at the Princeton Environmental Institute, Princeton University. SL acknowledges financial support from the China Scholarship Council (CSC). VRD acknowledges funding from Ramón y Cajal fellowship (RYC-2012-10970). BTW was supported by the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. DJC acknowledges funding from the National Science Centre, Poland (NN309 713340). WRLA was supported in part by NSF DEB 1714972.
SC-23.1 Climate and Environmental Sciences Division, BER
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