<p style="text-align: justify;"><strong>Aims</strong>: The aim of this study is to test a method to extrapolate vine water status (estimated by the water potential; <strong>Ψ</strong>) over a whole appellation (protected geographical indication). The spatial extrapolation is based on an empirical approach that combines a reference site (baseline measurements) and carbon isotope discrimination (δ¹³C) values as ancillary data (AD).</p><p style="text-align: justify;"><strong>Methods and results</strong>: Experiments were conducted on the whole Tavel appellation (Gard, France). The study focused on the dominant variety: Grenache. <strong>Ψ</strong> was measured as predawn leaf water potential and was monitored over three consecutive years, 2008, 2009 and 2010, on 10, 24 and 24 sites, respectively. δ¹³C measurements were made at harvest in 2010 on the 24 sites. The spatial model (SPIDERδ) was calibrated using Ydata from 2009 and 2010 and δ¹³C data from 2010. The quality of prediction was tested on the 2008 data, considered as an independent data set. The results show that SPIDERδ was relevant in estimating <strong>Ψ</strong> at the whole appellation scale. The extrapolation model significantly improves the prediction (R² = 0.88) compared to a conventional method based on <strong>Ψ</strong> averages across the appellation (R² = 0.66).</p><p style="text-align: justify;"><strong>Conclusion</strong>: Based on a single measurement taken at time «t» on a reference site, SPIDERδ makes it possible to estimate <strong>Ψ</strong> on all sites where a δ¹³C value is available. The use of AD like δ¹³C makes it possible to consider the spatial extrapolation of <strong>Ψ</strong> with higher spatial resolution than when only direct measurements are used to calibrate the model.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: This work demonstrates the value of using an AD like δ¹³C to assess <strong>Ψ</strong> at a scale larger than the single field. This significant result opens the door to the practical use of spatial extrapolation models with higher spatial resolution.</p>