Revealing Seasonal Plasticity of Whole-Plant Hydraulic Properties Using Sap-Flow and Stem Water-Potential Monitoring

Abstract. Plant hydraulic properties are critical to predicting vegetation water use as part of land-atmosphere interactions and plant responses to drought. However, current measurements of plant hydraulic properties are labour-intensive, destructive, and difficult to scale up, consequently limiting the comprehensive characterization of whole-plant hydraulic properties and hydraulic parameterization in land-surface modelling. To address these challenges, we develop a method, a pumping-test analogue, using sap-flow and stem water-potential data to derive whole-plant hydraulic properties, namely maximum hydraulic conductance, effective capacitance, and Ψ₅₀ (water potential at which 50 % loss of hydraulic conductivity occurs). Experimental trials on Allocasuarina verticillata indicate that the parameters derived over short periods (around 7 days) exhibit good representativity for predicting plant water use over at least one month. We applied this method to estimate near-continuous whole-plant hydraulic properties over one year, demonstrating its potential to supplement existing labour-intensive measurement approaches. The results reveal the seasonal plasticity of the effective plant hydraulic capacitance. They also confirm the seasonal plasticity of maximum hydraulic conductance and the hydraulic vulnerability curve, known in the plant physiology community while neglected in the hydrology and land-surface modelling community. It is found that the seasonal plasticity of hydraulic conductance is associated with climate variables, providing a way forward to represent seasonal plasticity in models. The relationship between derived maximum hydraulic conductance and Ψ₅₀ also suggests a trade-off between hydraulic efficiency and safety of the plant. Overall, the pumping-test analogue offers potential for better representation of plant hydraulics in hydrological modelling, benefitting land-management and land-surface process forecasting.