The forecasted groundwater resource depletion under future climatic conditions will greatly influence subsurface groundwater dependent ecosystems and their associated vegetation. In the Mediterranean region this will create harsh conditions for the maintenance of agroforestry systems dependent on groundwater, such as cork oak woodlands. The threat of increasing aridity conditions will affect their productivity and eventually induce a shift in their geographical distribution. Thus, characterizing and modelling the relationship between environmental conditions and subsurface groundwater dependent vegetation (subsurface GDV) will allow to identify the main drivers controlling its distribution and predict future impacts of climate change. In this study, we built a model that explains subsurface GDV distribution in southern Portugal from climatic, hydrological and topographic environmental variables. To achieve this, we relied on the density of Quercus suber, Quercus ilex and Pinus pinea as proxy species of subsurface GDV. Model fitting was performed between the proxy species Kernel density and the selected environmental predictors using (1) a simple linear model and (2) a Geographically Weighted Regression (GWR), to account for auto-correlation of the spatial data and residuals. When comparing the results of both models, the GWR modelling results showed improved goodness of fitting, as opposed to the simple linear model. Soil type was the main driver of subsurface GDV density closely followed by the aridity index. Groundwater depth did not appear to be as pertinent in the model as initially expected. Model predictor coefficients were used as weighting factors for multicriteria analysis, to create a suitability map to the subsurface GDV in southern Portugal. A validation of the resulting map was performed using independent data of integrated potential distribution of each proxy tree species in the region and overall, there was an accordance between areas of good suitability to subsurface GDV. The model was considered reliable to predict the distribution of the studied vegetation, however, lack of data quality and information was shown to be the main cause for suitability discrepancies between maps. Our new methodology on mapping of subsurface GDV's will allow to predict the evolution of the distribution of subsurface GDV according to climate change scenarios and aid stakeholder decision-making concerning priority areas of water resources management.
