Abdennabi Alitane

Flooding is one of the most damaging natural hazards worldwide, particularly in data-scarce watersheds where long-term hydrometeorological records are limited. This study focuses on the R’Dom watershed in the Meknes region, Morocco, and aims to improve flood susceptibility and relative flood risk assessment by integrating remote sensing, Geographic Information Systems (GIS), and multi-criteria decision-making (MCDM) approaches. The Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP/FAHP) were applied to evaluate flood hazard, vulnerability, and overall risk using seventeen conditioning factors, including topographic, hydrological, geological, land-cover, socio-economic, and infrastructure-related variables. The Flood Hazard Index (FHI), Flood Vulnerability Index (FVI), and Flood Risk Index (FRI) were calculated to produce flood susceptibility, vulnerability, and relative flood risk maps. Model validation was performed using a point-based flood inventory dataset composed of 900 locations, including 450 flood and 450 non-flood points, compiled from historical flood information, field observations, local information, official reports, and satellite-based interpretation. The dataset was divided into 70% for training and 30% for testing, and model performance was assessed using receiver operating characteristic–area under the curve (ROC-AUC) analysis. The AHP and Fuzzy AHP models showed good to excellent predictive performance, with testing AUC values ranging from 0.767 to 0.935. The AHP-based models achieved the highest testing performance, while Fuzzy AHP remained useful for representing uncertainty in expert judgment and gradual spatial transitions. The final flood risk map indicates that approximately 18.78% of the study area, corresponding to 240.62 km², is classified as having a high to very high flood risk, mainly around the Meknes conurbation and locally near the El Hajeb region. These results provide a practical decision-support tool for identifying priority areas for flood mitigation, land-use planning, and watershed management. However, the proposed GIS–MCDA approach produces relative flood susceptibility and risk classes and does not replace hydrological or hydraulic modeling for estimating flood depth, discharge, inundation extent, or return-period-based flood hazard.

The Saiss plain in northern Morocco covers an area of 2300 km² and is one of the main agricultural contributors to the national economy. However, climate change and water scarcity reduce the region’s agricultural yields. Conventional methods of estimating evapotranspiration (ET) provide localized results but cannot capture regional-scale variations. This study aims to estimate the spatiotemporal evolution of daily crop ET (olives, fruit trees, cereals, and vegetables) across the Saiss plain. The METRIC model was adapted for the region using Landsat 8 data and was calibrated and validated using in situ flux tower measurements. The methodology employed an energy balance approach to calculate ET as a residual of net radiation, soil heat flux, and sensible heat flux by using hot and cold pixels for calibration. METRIC-ET ranged from 0.1 to 11 mm/day, demonstrating strong agreement with reference ET (R² = 0.76, RMSE = 1, MAE = 0.78) and outperforming MODIS-ET in accuracy and spatial resolution. Olives and fruit trees showed higher ET values compared to vegetables and cereals. The results indicated a significant impact of ET on water availability, with spatiotemporal patterns being influenced by vegetation cover, climate, and water resources. This study could support the development of adaptive agricultural strategies.

The Ouislane sub-watershed is currently experiencing severe water shortages and is highly dependent on its water supply. The sub-watershed spans two communes: Meknes to the north and El Hajeb to the south. It serves as the primary water source for irrigation and drinking purposes for the local population. Consequently, it is crucial to assess the spatio-temporal variations of water quality to identify and address potential gaps; these focused on effective monitoring systems to detect contaminants, pollutants and health risks. This research project aims on the application of self-organizing map (SOM) techniques combined with cluster analysis to classify water quality in springs for drinking and irrigation purposes. The present study evaluates the water quality variations using physicochemical parameters of twelve water springs, collected during the wet and dry seasons of 2022. For this purpose, the water quality index (WQI), self-organizing map (SOM), hierarchical cluster analysis (HCA), and principal component analysis (PCA) are used as evaluation and classification methods. As a result, the SOM algorithm with a size of 5 × 5 units identified as the most suitable, based on the minimum quantization error (QE) and topographic error (TE), yielding a QE of 0.379 and a TE of 0.000. It grouped the water quality data into five distinct clusters, Cluster I represented 37.5% of the total samples, while cluster II represented 25%. Cluster III and IV each accounted for 8.33% of the samples, while 20.83% of the sampling water are classified in cluster V. Clusters I, II, and IV indicate good water suitable for drinking. However, cluster V had the highest WQI, suggesting very high contamination due to increased levels of the 10 studied physicochemical parameters. The water quality in this region (cluster V) is influenced by natural processes, such as precipitation intensity, weathering and vegetation cover, as well as anthropogenic factors like agriculture and urban concentration. PCA confirmed the clustering results obtained by SOM. However, SOM provides a more detailed classification and additional insights into the dominant variables influencing the classification processes. The results of this study suggest that SOM was an effective tool for gaining a better understanding of the patterns and processes driving water quality in the Ouislane sub-watershed and provides valuable avenues for further research to establish and monitor water quality for effective management of water resources.