Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid/ensemble machine learning (ML) models are developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector Machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, and DRASTIC-RF-MLP, for groundwater pollution assessment in the Saiss basin, in Morocco. The performances of these models are evaluated using the Receiver Operating Characteristic curve (ROC curve), precision, and accuracy. Based on the results of the ROC curve method, it is indicated that the use of hybrid/ensemble machine learning (ML) models improves the performance of the individual machine learning (ML) algorithms. In effect, the AUC value of the original DRASTIC is 0.51. Furthermore, both hybrid/ensemble models, DRASTIC-RF-MLP (AUC = 0.953) and DRASTIC-RF-SVM, (AUC = 0.901) achieve the best accuracy among the other models, followed by DRASTIC-RF (AUC = 0.852), DRASTIC-SVM (AUC = 0.802), and DRASTIC-MLP (AUC = 0.763). The results delineate areas vulnerable to pollution, which require urgent actions and strategies to improve the environmental and social qualities for the local population.

Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of “where and why” gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area.

The Eddy Covariance station provides observations of agrometeorological variables and surface energy fluxes, collected from 2019 to 2022, in a citrus orchard located in the Souss-Massa plain, Morocco. The present dataset comprises measurements recorded via a set of aboveground and subsurface sensors. The aboveground setup consistently measures air temperature, relative humidity, wind speed, net radiation, and precipitation. Additionally, the subsurface setup continuously tracks soil temperature, moisture, and electrical conductivity at depths from 5 to 80 cm, along with soil heat flux. Moreover, these setups enable the measurement of turbulent fluxes (sensible and latent heat). Given the limited availability of long-term agrometeorological data in semi-arid regions of the Mediterranean, this paper addresses a critical data gap by providing a reliable agrometeorological dataset. The latter consists of two types of data: 30 min interval files and high-frequency files (20 Hz, i.e., one measurement every 50 ms). The processing of this data involved Card Convert, MATLAB EC-Pack, and Excel, with data quality control performed by removing outliers and excluding nighttime fluxes. The dataset is organized in a table and provided in a .csv format with standard metadata. It is designed for a wide range of applications, including evapotranspiration modeling, satellite product validation, agroclimatic monitoring, determining crop irrigation requirements, precision irrigation planning, and water management. Additionally, the dataset can be reused for crop and hydrological model calibration, as well as soil moisture and crop stress prediction using machine learning algorithms.

Water resources worldwide are increasingly threatened by growing anthropogenic pressures and inherent hydrogeological constraints, raising concerns about their suitability for domestic use. This study aims to assess the physicochemical quality of certain springs in the Agadir Ida-Ou-Tanane region and evaluate compliance with international thresholds established by the World Health Organization (WHO) for drinking water. A total of twenty-six water samples were collected across the studied region and analyzed for key parameters including Electrical Conductivity (EC), Total Dissolved Solids (TDS) and Total Hardness (TH). The EC values ranged from 275 µS/cm to 4210 µS/cm with an average of 1446.15 µS/cm. For Total dissolved solids, values ranged from 135 ppm to 7140 ppm, with Total hardness presented a maximum value of 3217.02 mg/L and minimum value of 188.9 mg/L. Water Quality Index (WQI) was calculated to provide an integrated evaluation of the overall water quality.Spatial distribution of water quality was further examined through Inverse Distance Weighting (IDW) interpolation. WQI based classification revealed that 73.1% of the springs were in acceptable quality categories, with 34.6% classified as excellent and 38.5% as good. Despite this generally favorable status, TDS values approach or exceed international thresholds in several locations, indicating the need for region-wide monitoring and treatment strategies. Considering the heavy dependence of rural communities on spring water, these findings underscore the importance of investing in adequate treatment infrastructure and implementing robust protection measures for sustainable water resource management.

Water stress and groundwater overexploitation constitute critical challenges to water sustainability in semi-arid regions. In Morocco, the Saïss Basin represents a strategic agricultural area facing increasing pressure on groundwater resources due to intensive irrigation, fragmented governance, and limited coordination among water stakeholders. This study assesses the effectiveness of stakeholder’s integration within the Integrated Water Resources Management (IWRM) framework, with a focus on governance, participation, and decision-making processes. A mixed-methods approach was adopted, combining stakeholder surveys, institutional analysis, and a SWOT-based evaluation to address both technical and socio-institutional dimensions of water management. Key stakeholders’ groups, including public authorities, water agencies, agricultural users, and local organizations, were analyzed in terms of their roles, interactions, and influence on groundwater management. The results reveal a persistent gap between IWRM principles and their practical implementation, characterized by limited stakeholder coordination, uneven participation, and sectoral fragmentation. While institutional frameworks for IWRM exist, their operationalization remains constrained by power asymmetries, insufficient data sharing, and weak integration of local actors in decision-making. The SWOT analysis highlights opportunities for improving stakeholder engagement through participatory platforms, capacity building, and the integration of scientific knowledge into policy processes.This study provides evidence-based insights into the governance barriers hindering effective IWRM implementation in groundwater-dependent regions. The findings contribute to ongoing debates on adaptive water governance and offer practical recommendations to strengthen stakeholder integration as a pathway toward sustainable groundwater management and the achievement of SDG 6 in arid and semi-arid regions.