Monitoring water status at different points within a single field is time-consuming and expensive. Nevertheless, it is necessary to consider within-field variability since water status is usually highly variable and conditions greatly grape quality. To overcome this situation, models that allow estimation of the relative difference in vine water status between a reference point and other points in the field have been developed. The aim of this work is to evaluate the performance of a model developed in South Eastern France for the spatial prediction of vine water status to the conditions of a traditional rain-fed vineyard in Rioja Alavesa, Spain. The model proved to be suitable to estimate grapevine water status variability within a medium size vineyard (4.2 ha) under the traditional growing conditions in Rioja Alavesa (gobelet trained cv. Tempranillo vines with no irrigation), although it was necessary to include some water status related information [in this case carbon isotope ratio (δ13C) from the previous season] to improve the performance and applicability of the model.