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.

This study presents a comprehensive analysis of hydrological alterations and environmental flow components in the Beht River basin in northwest Morocco, using a coupled approach involving the Soil and Water Assessment Tool (SWAT) for hydrological modeling, the Indicators of Hydrologic Alteration (IHA) for flow regime assessment, and the Standardized Precipitation Index (SPI) for drought characterization. The SWAT model, run on a daily time step, showed satisfactory performance in terms of statistical criteria for both calibration and validation periods, despite encountering limitations, and proved its ability to simulate and reproduce the hydrological behavior of the basin. Using the IHA, we investigated changes in the hydrological regime over two distinct periods, revealing significant hydrological alteration. The SPI analysis supported these findings by highlighting the variable impacts of dry and wet periods on the hydrological regime, thus validating the observed changes in river flow indicators. As a preliminary step toward establishing environmental flows in the Beht River, this study provides foundational insights into the temporal evolution of its hydrology. These findings offer a valuable basis for better water resource management and conservation in the region.

Band selection techniques are widely explored in spectroscopic analysis to enhance predictive performance. This study aims to inform the design of the spectral dispersion component of a low-cost smartphone microspectrometer by identifying the most informative bands within practical constraints on the number of bands and spectral range. The goal is to enable rapid, affordable, and accurate leaf Nitrogen (N) assessment as part of the DEMETRA project, which promotes methodological and technological innovation for smart nutrient estimation in agriculture. Three band selection methods were evaluated: Permutation Feature Importance (PFI), Recursive Feature Elimination (RFE), and Genetic Algorithms (GA), each integrated with machine learning regression models. Among these, GA proved most effective, outperforming models based on the full spectral range. The results highlight the potential of targeted band selection for cost-effective, in-field spectroscopic tools.

Freshwater lakes in Mediterranean regions are highly sensitive to climatic variability, particularly to droughts intensified by rising temperatures and increasing atmospheric evaporative demand. This study investigates drought variability and ecosystem responses in the Trichonida basin, the largest natural freshwater system in Greece, using an integrated approach that combines the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple time scales with satellite-derived Normalized Difference Vegetation Index (NDVI), Crop Water Stress Index (CWSI), and lake surface water temperature. SPEI analysis revealed increasingly recurrent and persistent drought conditions in recent years, especially at medium- and long-term scales. NDVI exhibited pronounced seasonal variability and a moderate long-term increase at the basin scale, largely associated with agricultural activity and irrigation practices, while sharp declines were observed during severe drought episodes. CWSI showed strong seasonal patterns characterized by recurrent summer water stress events, but no significant long-term trend. Correlation analysis indicated positive relationships between NDVI and SPEI at medium- to long-term time scales, and significant negative correlations between CWSI and SPEI at short and medium time scales. A strong relationship between NDVI and CWSI further suggests the sensitivity of vegetation greenness to water stress, particularly during summer and autumn. Lake surface water temperature exhibited seasonal warming trends that coincided with periods of increased vegetation water stress. Drought-related water risks arise for calcareous fens dominated by Cladium mariscus in the Lake Trichonida system, a habitat of high conservation value, whose productivity is strongly seasonally controlled and closely linked to thermal dynamics. Overall, the combined multi-indicator analysis provides valuable insights into drought impacts and seasonal ecosystem vulnerability in Mediterranean lake basin environments, highlighting the importance of integrated monitoring frameworks for sustainable freshwater ecosystem management under increasing climatic variability.