Source water apportionment studies using the dual isotopes of oxygen and hydrogen have revolutionized our understanding of ecohydrology. But despite these developments—mostly over the past decade—many technical problems still exist in terms of linking xylem water to its soil water and groundwater sources. This is mainly due to sampling issues and possible fractionation of xylem water. Here we explore whether or not leaf water alone can be used to quantify the blend of rainfall event inputs from which the leaf water originates. Leaf water has historically been avoided in plant water uptake studies due to the extreme fractionation processes at the leaf surface. In our proof of concept work we embrace those processes and use the well-known Craig and Gordon model to map leaf water back to its individual precipitation event water sources. We also employ a Bayesian uncertainty estimation approach to quantify source apportionment uncertainties. We show this using a controlled, vegetated lysimeter experiment where we were able to use leaf water to correctly identify the mean seasonal rainfall that was taken up by the plant, with an uncertainty typically within ±1‰ for δ18O. While not appropriate for all source water studies, this work shows that leaf water isotope composition may provide a new, relatively un-intrusive method for addressing questions about the plant water source.

Source water apportionment studies using the dual isotopes of oxygen and hydrogen have revolutionized our understanding of ecohydrology. But despite these developments—mostly over the past decade—many technical problems still exist in terms of linking xylem water to its soil water and groundwater sources. This is mainly due to sampling issues and possible fractionation of xylem water. Here we explore whether or not leaf water alone can be used to quantify the blend of rainfall event inputs from which the leaf water originates. Leaf water has historically been avoided in plant water uptake studies due to the extreme fractionation processes at the leaf surface. In our proof of concept work we embrace those processes and use the well-known Craig and Gordon model to map leaf water back to its individual precipitation event water sources. We also employ a Bayesian uncertainty estimation approach to quantify source apportionment uncertainties. We show this using a controlled, vegetated lysimeter experiment where we were able to use leaf water to correctly identify the mean seasonal rainfall that was taken up by the plant, with an uncertainty typically within ±1‰ for δ18O. While not appropriate for all source water studies, this work shows that leaf water isotope composition may provide a new, relatively un-intrusive method for addressing questions about the plant water source.

Trees play a key role in the global hydrological cycle and measurements performed with the thermal dissipation method (TDM) have been crucial in providing whole-tree water-use estimates. Yet, different data processing to calculate whole-tree water use encapsulates uncertainties that have not been systematically assessed. We quantified uncertainties in conifer sap flux density (Fd ) and stand water use caused by commonly applied methods for deriving zero-flow conditions, dampening and sensor calibration. Their contribution has been assessed using a stem segment calibration experiment and 4 yr of TDM measurements in Picea abies and Larix decidua growing in contrasting environments. Uncertainties were then projected on TDM data from different conifers across the northern hemisphere. Commonly applied methods mostly underestimated absolute Fd . Lacking a site- and species-specific calibrations reduced our stand water-use measurements by 37% and induced uncertainty in northern hemisphere Fd . Additionally, although the interdaily variability was maintained, disregarding dampening and/or applying zero-flow conditions that ignored night-time water use reduced the correlation between environment and Fd . The presented ensemble of calibration curves and proposed dampening correction, together with the systematic quantification of data-processing uncertainties, provide crucial steps in improving whole-tree water-use estimates across spatial and temporal scales.