Mapping the suitability of groundwater-dependent vegetation in a semi-arid Mediterranean area

• Published in: Hydrology and earth system sciences Vol. 23; no. 9; pp. 3525 - 3552
• Main Authors: Gomes Marques, Inês, Nascimento, João, Cardoso, Rita M, Miguéns, Filipe, Condesso de Melo, Maria Teresa, Soares, Pedro M. M, Gouveia, Célia M, Kurz Besson, Cathy
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 03.09.2019; Copernicus Publications
• Subjects: Anomalies; Arid regions; Aridity; Autocorrelation; Climate change; Climate change scenarios; Climate models; Climatic indexes; Coefficients; Decision making; Distribution; Drainage density; Drought; Ecosystems; Environmental aspects; Environmental changes; Environmental risk; Evolution; Extreme drought; Extreme values; Extreme weather; Geospatial data; Global temperature changes; Goodness of fit; Groundwater; Groundwater management; Groundwater resources; Hydrologic models; Hydrology; Mapping; Multiple criterion; Observations; Pine trees; Predictions; Proxy; Regression analysis; Resource management; Spatial data; Species; Strategic management; Vegetation; Vegetation dynamics; Vegetation index; Water; Water management; Water resource management; Water resources; Water resources management; United States; Portugal
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-23-3525-2019

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.