In semi-arid areas, many ecosystems and activities depend essentially on water availability. In Morocco, the increase of water demands combined to climate change induced decrease of precipitation put a lot of pressure on groundwater. This paper reports the results of updating and evaluation of groundwater datasets with regards to climate scenarios and institutional choices. The continuous imbalance between groundwater extraction and recharge caused a dramatic decline in groundwater levels (20 to 65 m in the past 30 years). Additionally, Morocco suffers from the degradation in groundwater quality due to seawater intrusion, nitrate pollution and natural salinity changes. Climate data analysis and scenarios predict that temperatures will increase by 2 to 4 °C and precipitation will decrease by 53% in all catchments over this century. Consequently, surface water availability will drastically decrease, which will lead to more extensive use of groundwater. Without appropriate measures, this situation will jeopardize water security in Morocco. In this paper, we zoom on the case the Souss-Massa basin, where management plans (artificial recharge, seawater desalination, and wastewater reuse) have been adopted to restore groundwater imbalance or, at least, mitigate the recorded deficits. These plans may save water for future generations and sustain crop production.

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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.

Monitoring water quality in large dams is becoming a necessity for protecting stored water from various forms of pollution. This process requires analysis of several samples on a weekly or monthly basis. Our study aims to determine the relationship between water quality parameters (WQP) and digital data from the Sentinel-2 satellite to estimate and map the WQP in the Bin El Ouidane Reservoir. The in situ sampling was carried out in the Bin El Ouidane Reservoir (Azilal Province), followed by analysis of physicochemical parameters in the laboratory.These measurement results were compared with the reflectance in each sampling location to investigate the correlations between bands and laboratory chemical analysis results. The correlation results showed that all studied parameters have an R 2 greater than 0.52, and they can be transformed to predictive models by stepwise regression.The accuracy of our proposed models was tested using the Oum Er-Rbia Hydraulic Basin Agency data, and the results showed that only three parameters yield admissible verification results (Chlorophyll A, dissolved Oxygen and Nitrate). Those models were then used in geographic information system software to produce a thematic map of each parameter over the entire surface of the reservoir. As a conclusion, the Sentinel-2 images could help indicate the eutrophication stage in the Bin El Ouidane Reservoir, which is a major risk in major Moroccan reservoirs.