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).

The aim of this study is to identify suitable landfill sites in Kenitra Province, Morocco, using ArcGIS 10.8, Remote Sensing, and the Analytic Hierarchy Process (AHP) to enhance site selection and improve environmental and public health outcomes. Additionally, the study seeks to enhance decision-making through multicriteria analysis, align landfill site selection with Sustainable Development Goals (SDGs) to promote long-term sustainability, and provide a model that can be replicated in other Moroccan regions or countries facing similar waste management challenges. Ten criteria were considered in selecting new landfill sites, including distance from surface water, soil permeability, land use land cover (LULC), topographic wetness index, distance from the shoreline, slope, aspect, proximity to urban areas, transportation networks, and villages. Maps for each criterion were generated, and pairwise comparisons were used to assign weights to these factors. A Weighted Overlay Analysis (WOA) was then applied to create a landfill suitability map, classifying areas into high, moderate, low, and unsuitable categories. The results reveal a limited availability of highly suitable landfill areas within the province. To address this, the study proposes two strategically located waste management sites, A and B, to mitigate land constraints. Establishing these sites as centralized landfills for multiple municipalities aligns with Sustainable Development Goals (SDGs) 3, 6, 14, 15, and 17, promoting public health, preventing water pollution, protecting ecosystems, and fostering partnerships for sustainable waste management. This comprehensive approach emphasizes the need for collaboration in achieving long-term sustainability and environmental conservation.

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.