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Mapping the suitability of groundwater-dependent vegetation in semi-arid Mediterranean areas is fundamental for the sustainable management of groundwater resources and groundwater-dependent ecosystems (GDEs) under the risks of climate change scenarios. For the present study the distribution of deep-rooted woody species in southern Portugal was modeled using climatic, hydrological and topographic environmental variables. To do so, Quercus suber, Quercus ilex and Pinus pinea were used as proxy species to represent the groundwater-dependent vegetation (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 autocorrelation of the spatial data and residuals. When comparing the results of both models, the GWR modeling results showed improved goodness of fit as opposed to the simple linear model. Climatic indices were the main drivers of GDV density, followed by a much lower influence by groundwater depth, drainage density and slope. Groundwater depth did not appear to be as pertinent in the model as initially expected, accounting only for about 7% of the total variation compared to 88% for climate drivers.

The relative proportion of model predictor coefficients was used as weighting factors for multicriteria analysis to create a suitability map for the GDV in southern Portugal showing where the vegetation most likely relies on groundwater to cope with aridity. A validation of the resulting map was performed using independent data of the normalized difference water index (NDWI), a satellite-derived vegetation in-dex. June, July and August of 2005 NDWI anomalies, for the years 1999-2009, were calculated to assess the response of active woody species in the region after an extreme drought. The results from the NDWI anomalies provided an overall good agreement with the suitability to host GDV. The model was considered to be reliable for predicting the distribution of the studied vegetation.

The methodology developed to map GDVs will allow for the prediction of the evolution of the distribution of GDV according to climate change and aid stakeholder decision-making concerning priority areas of water resource management.