This research work is intended as a contribution to the development of a multicriteria methodology, combining several factors to control the availability of groundwater resources, in order to optimize the choice of location of future drilling and increase the chances to take water from productive structures which will satisfy the ever-increasing water demand of local population (Arghen basin in the Western Anti-Atlas chain of Morocco). The geographic information system is used to develop thematic maps describing the geometry and the hydrodynamic functioning of the aquifer. In this study, 11 factors including geology, topography, and hydrology, influencing the distribution of water resources were used. Based on the Analytical Hierarchy Process (AHP) model, GIS, and remote sensing, the study mapped and classified areas according to their hydrogeological potential. The favorable potential sectors cover 17% of the total area of the basin. The medium potential sectors account for 64%, while the unfavorable areas cover 18% of the basin area. The groundwater potential map of the study area has been validated by comparing with data from 159 boreholes scattered throughout the basin.

This study is focused on developing an approach for spatial mapping of groundwater by considering four types of factors (geological, topographical, hydrological, and climatic factors), and by using different bivariate statistical models, such as frequency ratio (FR) and Shannon’s entropy (SE). The developed approach was applied in a fractured aquifer basin (Ameln Basin, Western Anti-Atlas, Morocco), to map the spatial variation of groundwater potential. Fifteen factors (15) influencing groundwater were considered in this study, including slope degree, slope aspect, elevation, topographic wetness index (TWI), slope length (LS), topographic position index (TPI), plane curvature, profile curvature, drainage density, lineament density, distance to rivers and fault network, normalized difference vegetation index (NDVI), lithology, and land surface temperature (LST). The potential maps produced were then classified into five classes to illustrate the spatial view of each potential class obtained. The predictive capacity of the frequency ratio and Shannon’s entropy models was determined using two different methods, the first one based on the use of flow data from 49 boreholes drilled in the study area, to test and statistically calibrate the predictive capacity of each model. The results show that the percentage of positive water points corresponds to the most productive areas (high water flow) (42.86% and 30.61% for the FR and SE models, respectively). On the other hand, the low water flows are consistent with the predicted unfavorable areas for hydrogeological prospecting (4.08% for the FR model and 6.12% for the SE model). Additionally, the second validation method involves the integration of 7200 Hz apparent resistivity data to identify conductive zones that are groundwater circulation zones. The interpretation of the geophysical results shows that the high-potential zones match with low apparent resistivity zones, and therefore promising targets for hydrogeological investigation. The FR and SE models have proved very efficient for hydrogeological mapping at a fractured basement area and suggest that the northern and southern part of the study area, specifically the two major fault zones (Ameln Valley in the north, and the Tighmi-Tifermit Valley in the south) has an adequate availability of groundwater, whereas the central part, covering the localities of Tarçouat, Boutabi, Tililan, and Ighalen, presents a scarcity of groundwater. The trend histogram of the evolution of positive water points according to each potentiality class obtained suggests that the FR model was more accurate than the SE model in predicting the potential groundwater areas. The results suggest that the proposed approach is very important for hydrogeological mapping of fractured aquifers, and the resulting maps can be helpful to managers and planners to generate groundwater development plans and attenuate the consequences of future drought.

Accurate and spatially distributed precipitation data are fundamental to effective water resource management. In Morocco, as in other arid and semi-arid regions, precipitation exhibits significant spatial and temporal variability. Indeed, there is an intra- and inter-annual variability and the northwest is rainier than the rest of the country. In the Bouregreg watershed, this irregularity, along with a sparse gauge network, poses a major challenge for water resource management. In this context, remote sensing data could provide a viable alternative. This study aims precisely to evaluate the performance of four gridded daily precipitation products: three IMERG-V06 datasets (GPM-F, GPM-L, and GPM-E) and a reanalysis product (ERA5). The evaluation is conducted using 11 rain gauge stations over a 20-year period (2000–2020) on various temporal scales (daily, monthly, seasonal, and annual) using a pixel-to-point approach, employing different classification and regression metrics of machine learning. According to the findings, the GPM products showed high accuracy with a low margin of error in terms of bias, RMSE, and MAE. However, it was observed that ERA5 outperformed the GPM products in identifying spatial precipitation patterns and demonstrated a stronger correlation. The evaluation results also showed that the gridded precipitation products performed better during the summer months for seasonal assessment, with relatively lower accuracy and higher biases during rainy months. Furthermore, these gridded products showed excellent performance in capturing different precipitation intensities, with the highest accuracy observed for light rain. This is particularly important for arid and semi-arid regions where most precipitation falls under the low-intensity category. Although gridded precipitation estimates provide global coverage at high spatiotemporal resolutions, their accuracy is currently insufficient and would require improvement. To address this, we employed an artificial neural network (ANN) model for bias correction and enhancing raw precipitation estimates from the GPM-F product. The results indicated a slight increase in the correlation coefficient and a significant reduction in biases, RMSE, and MAE. Consequently, this research currently supports the applicability of GPM-F data in North Western Morocco.

This study aims to investigate the groundwater vulnerability concerning potentially toxic elements in the vicinity of the abandoned iron mine of Ouixane (Morocco). A modified DRASTIC method (DRSTI) is proposed with satisfactory results. High vulnerability zones represent 40% of the study area, while medium and low vulnerability represent, respectively, 42% and 18% of the study area. These results have been validated by groundwater geochemical analyses of potentially toxic elements carried out in the framework of previous studies in the same area. Thus, the superposition of the waste rock and tailings map with the vulnerability map showed that the latter is located in areas of high to medium vulnerability and therefore constitutes the main cause of the deterioration of the geochemical quality of groundwater. Otherwise, the vulnerability method showed that the main parameters that significantly affect the vulnerability are: the depth of water (D), net recharge (R), and the unsaturated zone (I), while the other parameters do not significantly affect the model used and do not have much influence on the results of the vulnerability assessment. The method used allowed us to locate the most vulnerable areas to potentially metallic toxic elements pollution resulting from the abandoned iron mine of Ouixane, and it constitutes a tool for decision support and for developing effective action plans to mitigate and monitor the effects of the transfer of potentially toxic elements pollution to groundwater.

Purpose. The Bou Azzer Mine encounters difficulties during cobalt mining. In order to select the optimal mining sequence with the least geotechnical stability problems, one possible variant is the cut and backfill mining method used in the Bou Azzer East area at a depth of 540 m. Methods. This paper presents a methodology for selecting a sequence of the cut and backfill mining method using 2D geotechnical numerical modeling, taking into account the morphological characteristics, geomechanical properties of the ore and the surrounding rocks. Findings. The sequences of mining with rock backfill and rock-cemented backfill show that the high principal stress (Sigma 1) is in the range of 10-153 MPa, and the safety factors are in the range of 0.63-1.89. Therefore, mining sequences with cemented backfill and under cemented backfill have a principal stress (Sigma 1) in the range of 10-112 MPa and acceptable safety factors. Originality. In this study, the bottom-up mining sequence with a cemented backfill is proposed for the case of low-quality serpentine footwall. This mining sequence aims to achieve good cobalt mine production and provides a safe environment for miners. Practical implications. In the mining industry, the choice of mining method using 2D or 3D geotechnical numerical mo-deling is important to ensure the safest and most operational mining sequence in the mine lifetime.