Remote sensing (RS) data and geographic information system (GIS) techniques were used to monitor the changes in the Oasis agroecosystem of the pre-Saharan province of Errachidia, southeastern Morocco. The land use and land cover (LULC) change of the agroecosystem of this province was processed using Landsat time series with 5-year intervals of the last thirty years. The normalized difference vegetation index (NDVI) and the maximum likelihood classification (MLC) were categorized into five classes, including water bodies, cultivated land, bare land, built-up, and desertified land. The overall accuracy of the MLC maps was estimated to be higher than 90%. The finding showed a degradation trend represented by an increase in desertified lands, which tripled in the ten last years, passing from 20.62% in 2011 to 58.49% in 2022. The findings also depicted a decreasing trend in the cultivated area in this period passing from 174.2 ${\mathrm{km}}^2$ in 1991 to 82.2 ${\mathrm{km}}^2$ in 2022. Using NDWI, Landsat images from 1991 to 2021 depicted a strong association between the water reserve in Hassan Eddakhil dam in the upstream area and the LULC changes. The oases from the dam (upstream) to Er-Rissani (downstream) recorded high rates of decline with an increasing trend of desertification due to drought and overuse mainly of groundwater. The outputs of this research effort constitute a significant source of information that may be used to support further research and decision-makers to manage arid ecosystems and achieve the sustainable development goals (SDGs), precisely the SDGs 15 (Life on land).

Groundwater plays a crucial role in sustaining agriculture and livelihoods in the arid Middle Draa Valley (MDV) of southeastern Morocco. However, increasing groundwater extraction, declining rainfall, and the absence of effective floodwater harvesting systems have led to severe aquifer depletion. This study applies and compares six machine learning (ML) algorithms—decision trees (CART), ensemble methods (random forest, LightGBM, XGBoost), distance-based learning (k-nearest neighbors), and support vector machines—integrating GIS, satellite data, and field observations to delineate zones suitable for groundwater recharge. The results indicate that ensemble tree-based methods yielded the highest predictive accuracy, with LightGBM outperforming the others by achieving an overall accuracy of 0.90. Random forest and XGBoost also demonstrated strong performance, effectively identifying priority areas for artificial recharge, particularly near ephemeral streams. A feature importance analysis revealed that soil permeability, elevation, and stream proximity were the most influential variables in recharge zone delineation. The generated maps provide valuable support for irrigation planning, aquifer conservation, and floodwater management. Overall, the proposed machine learning–geospatial framework offers a robust and transferable approach for mapping groundwater recharge zones (GWRZ) in arid and semi-arid regions, contributing to the achievement of Sustainable Development Goals (SDGs))—notably SDG 6 (Clean Water and Sanitation), by enhancing water-use efficiency and groundwater recharge (Target 6.4), and SDG 13 (Climate Action), by supporting climate-resilient aquifer management.

In September 2024, following two rare storms, Lake Iriqui in southern Morocco experienced a remarkable revival after five decades of desiccation. Historically, the lake played an important role as one of the largest water bodies before the Sahara Desert, serving as a critical stopover in migratory routes for various bird species. Two field missions documented this event: the first confirmed the lake’s reappearance, while the second recorded the resurgence of the ecosystem and the return of migratory birds, last observed in the lake in 1968. The lake’s surface water extent, which had been completely dry, expanded dramatically, reaching over 80 km² after the first storm and subsequently increasing to approximately 146 km² following the second. This event has drawn considerable attention from international and national media. The revival was monitored using satellite imagery from Landsat 8 and 9 and Sentinel-2A, processed through Google Earth Engine (GEE), with the Normalized Difference Water Index (NDWI) applied to detect water presence. A time-series analysis revealed significant changes in the lake’s surface water extent following the rainfall. This study emphasizes the need for proactive support to preserve Lake Iriqui, aligning with sustainable development goals: SDG 15 (Life on Land) and SDG 8 (Decent Work and Economic Growth). These goals highlight the importance of sustainable water resource management, biodiversity conservation, and eco-tourism initiatives to benefit local communities.

La nappe phréatique de Mnasra, située sur le littoral du Gharb, dans le Nord-Ouest du Maroc, constitue un réservoir important d'eau douce. Elle a été, durant les deux dernières décennies, à la base d'un développement fondé sur la culture intensive sous irrigation. Celle-ci se caractérise par l'utilisation intense des engrais chimiques représentant, en parallèle avec les pesticides, un risque majeur pour la durabilité des ressources en eau souterraine. Afin d'évaluer l'impact de l'intensification de l'utilisation du sol sur la qualité des eaux souterraines, la présente étude propose d'évaluer et de spatialiser la sensibilité de la nappe de Mnasra vis-à-vis de la pollution nitrique à l'aide de la modélisation couplée au SIG. L'application de deux modèles, le modèle DRASTIC (L. ALLER et al. 1987) et le modèle SI (L. RIBEIRO, 2000), a permis de comparer entre eux les résultats des deux méthodes et de les évaluer en fonction de la réalité terrain. La carte de la susceptibilité obtenue par le modèle SI s'avère plus proche des conditions réelles. Elle peut être considérée comme un outil assez efficace, quoique perfectible, pour estimer les risques de dégradation de la qualité des eaux souterraines.

The Sebou Basin is one of the most important hydrographic basins in Morocco. When the Wadi Sebou enters the Gharb Plain, it encounters difficulties in evacuating its waters during wet years due to its flattening, resulting in risks of floods. Its flow decreases during drought years and in August and September. For the purpose of this research, we were granted authorization to utilize the hydrometric station located in Bel-Ksiri. This station is most suitable for the study as it was designated as a reference point for the Wadi Sebou's flooding in the lower basin. This study is based on detecting floods in the Wadi Sebou from 1976 to 2020, dividing this period on pre and post 1997, corresponding to periods before and after the construction of the Al Wahda dam, examining the mean daily flow rates. Identifying discontinuities within the maximum daily flow series from 1967 to 2020 was achieved in Microsoft Excel. Additionally, determining flood return periods and examining low water flows are integral aspects of the study. The findings indicate a relative decrease in the frequency and intensity of floods within the Wadi Sebou's basin, coupled with effective control of low water levels. These changes are attributed to the construction of the Al Wahda dam, which guarantees a stable water supply for irrigation purposes in the plain. To ensure the long-term sustainability of water management practices, the adoption of innovative irrigation technologies is deemed necessary.

The authors, with the help of a team of researchers, have discovered twelve rock shelters with inside paintings on the southern slopes of the Jbel Bani Mountains in southern Morocco. The paintings vary in subject and time period and span multiple rock art styles. Majestic creatures that once inhabited southern Morocco are depicted next to hunters, pastoralists, and warriors. The shelters and paintings cast upon their walls illustrate a transfer of culture, beliefs, technology, and ideas between people groups of the Meridional and Central Sahara and the Jbel Bani region. These discoveries were all made along a mountain path in the Bani Mountains known as Foum Laachar and may help trace ancient human migration routes.