Drought is a severe disaster, increasingly exacerbated by climate change, and poses significant challenges worldwide, particularly in arid and semi-arid regions like Morocco. This study aims to assess and monitor drought using a multi-index approach to provide a comprehensive understanding of its spatio-temporal dynamics at both meteorological and agricultural levels. The research focuses on the Upper Oum Er Rabia watershed, which spans 35,000 km² and contributes approximately a quarter of Morocco’s renewable water resources. We propose a methodology that combines ERA5 temperature data from remote sensing with ground-based precipitation data to analyze drought characteristics. Three meteorological indices were utilized: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). Additionally, three remote-sensing indices were employed to capture agricultural drought: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Crop Water Stress Index (CWSI), with a total of 528 NDVI and EVI images and 1016 CWSI images generated through Google Earth Engine (GEE), using machine-learning techniques. Trend analyses were conducted to monitor drought patterns spatio-temporally. Our results reveal that the three-month interval is critical for effective drought monitoring and evaluation. Among the indices, SPEI emerged as the most effective for capturing drought in combination with remote-sensing data, while CWSI exhibited the highest correlation with SPEI over the three-month period, outperforming NDVI and EVI. The trend analysis indicates a significant precipitation deficit, alongside increasing trends in temperature and evapotranspiration over both the short and long term. Furthermore, all drought indices (SPI, SPEI, and RDI) demonstrate an intensification of drought conditions. Adaptation strategies are essential for managing water resources in the Upper Oum Er Rabia watershed under these evolving climate conditions. Continuous monitoring of climate variables and drought indices will be crucial for tracking changes and informing future water management strategies.

Understanding the hydrological responses of semi-arid watersheds to climatic variability is crucial for sustainable water resource management. This study demonstrates that integrating remote-sensing products with traditional hydrological indices offers a robust approach for comprehensive drought monitoring . An approach using multi-index was adopted by combining different remote-sensing products including the Automated Water Extraction Index (AWEI), the Normalized Difference Water Index (NDWI), and the Crop Water Stress Index (CWSI) computed over 10 years (2013–2022) using Google Earth Engine (GEE), with hydrological metrics such as the Standardized Runoff Index (SRI), the Streamflow Drought Index (SDI), and the Standardized Precipitation–Evapotranspiration Index (SPEI) analyzed over 44 years (1979–2022). Our findings reveal that remote-sensing products, particularly AWEI and NDWI, align most strongly with hydrological drought indices over 6 and 12 months, with AWEI–SRI/SDI correlations reaching r = 0.51/0.60 at 6 months and NDWI–SRI/SDI reaching r = 0.52/0.50 at 12 months. Although SPEI exhibits modest correlations with AWEI and NDWI (not exceeding r = 0.50), its integration with remote-sensing hydrological indices remains secondary. Trend analyses indicate significant declines in surface water extent during the study period: AWEI decreased by 1.03 km2/year and NDWI by 0.73 km2/year, underscoring intensifying drought conditions. These results highlight the indispensable role of remote sensing in delivering continuous, detailed spatio-temporal insights and supporting adaptive water governance strategies, particularly in regions facing increasing water scarcity.

The Inaouène watershed (3600 km²), part of the Sebou River’s upper valley in northern Morocco, faces urban, agricultural, and industrial discharges. This research investigates the environmental impact of heavy metal contamination in sediments and its implications for sustainable watershed management and long-term ecological protection. Sediment samples were collected from six sites along the river and the Idriss 1st dam. A combined approach of geochemical analyses and multivariate statistical methods (PCA, HCA) identified metal sources and grouped sites by contamination patterns. Additionally, pollution indices (I_geo, EF, PLI) were used to assess contamination levels and infer potential sources. Results revealed variable metal concentrations: upstream (Ech1) showed high levels of chromium (133 mg/kg) and copper (32.5 mg/kg) linked to urban discharges and erosion, while downstream (Ech6) exhibited high barium (3245 mg/kg) and strontium (505 mg/kg) concentrations due to dam sedimentation. Pollution indices confirmed moderate to high contamination, particularly at Ech1 and Ech6. Multivariate analysis identified three main clusters influenced by both anthropogenic and geological factors. These findings underline the need for integrated sediment management, regular monitoring, and environmental protection strategies to preserve the watershed and the aquifer’s ecological balance.

In order to evaluate the degree of sediments contamination by metallic trace elements in the watershed of Oued Inaouene (North-East of Morocco), samples of surface sediments were taken along the river and at the level of the Idriss 1st dam. The objective is to obtain a thorough knowledge of the...