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In South-western Morocco, water scarcity and high temperature are the main factors determining species survival. Argania spinosa (L.) Skeels is a tree species, endemic to Morocco, which is suffering from ongoing habitat shrinkage. Argan trees play essential local ecological and economic roles: protecting soils from erosion, shading different types of crops, helping maintain soil fertility and, even more importantly, its seeds are used by the local population for oil production, with valuable nutritional, medicinal and cosmetic purposes. The main objective of this study was to identify the sources of water used by this species and to assess the effect of water availability on the photosynthetic rate and stem water potential in two populations: one growing on the coast and a second one 10 km inland. Stem water potential, photosynthetic rate and xylem water isotopic composition (δ18O) were seasonally monitored during 2 years. Trees from both populations showed a similar strategy in the use of the available water sources, which was strongly dependent on deep soil water throughout the year. Nevertheless, during the wet season or under low precipitation a more complex water uptake pattern was found with a mixture of water sources, including precipitation and soil at different depths. No evidence was found of the use of either groundwater or atmospheric water in this species. Despite the similar water-use strategy, the results indicate that Argania trees from the inland population explored deeper layers than coastal ones as suggested by more depleted δ18O values recorded in the inland trees and better photosynthetic performance, hence suggesting that the coastal population of A. spinosa could be subjected to higher stress.

The drinking and irrigation water scarcity is a major global issue, particularly in arid and semi-arid zones. In rural areas, groundwater could be used as an alternative and additional water supply source in order to reduce human suffering in terms of water scarcity. In this context, the purpose of the present study is to facilitate groundwater potentiality mapping via spatial-modelling techniques, individual and ensemble machine-learning models. Random forest (RF), logistic regression (LR), decision tree (DT) and artificial neural networks (ANNs) are the main algorithms used in this study. The preparation of groundwater potentiality maps was assembled into 11 ensembles of models. Overall, about 374 groundwater springs was identified and inventoried in the mountain area. The spring inventory data was randomly divided into training (75%) and testing (25%) datasets. Twenty-four groundwater influencing factors (GIFs) were selected based on a multicollinearity test and the information gain calculation. The results of the groundwater potentiality mapping were validated using statistical measures and the receiver operating characteristic curve (ROC) method. Finally, a ranking of the 15 models was achieved with the prioritization rank method using the compound factor (CF) method. The ensembles of models are the most stable and suitable for groundwater potentiality mapping in mountainous aquifers compared to individual models based on success and prediction rate. The most efficient model using the area under the curve validation method is the RF-LR-DT-ANN ensemble of models. Moreover, the results of the prioritization rank indicate that the best models are the RF-DT and RF-LR-DT ensembles of models.