Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (<7.3%) with AUC = 95%. Following the analysis of LS mapping results, lithology, distance to the hydrographic network, distance to roads, and elevation were the four main factors controlling landslide susceptibility in the study area. Future mitigation and protection activities within the Aube department can benefit from the present study mapping results, implicating an optimized land management for decision-makers.

Dams are vital for irrigation, power generation, and domestic water needs, but siltation poses a significant challenge, especially in areas prone to water erosion, potentially shortening a dam’s lifespan. The Ahmed El Hansali Dam in Morocco faces heightened siltation due to its upstream region being susceptible to erosion-prone rocks and high runoff. This study estimates the siltation at the dam from its construction up to 2014 using bathymetric data and the Brown model, which is a widely-used empirical model that calculates reservoir trap efficiency. Additionally, the study evaluates the impact of Land Use and Land Cover (LULC) changes and projected future rainfall until around 2076 based on siltation rates. The results indicate that changes in LULC, particularly temporal variations in precipitation, have a significant impact on the siltation of the Ahmed El Hansali dam. Notably, rainfall is strongly correlated with the siltation rate, with an R² of 0.92. The efficiency of sediment trapping (TE) is 97.64%, meaning that 97.64% of the sediment in the catchment area is trapped or deposited at the bottom of the dam. The estimated annual specific sediment yield is about 32,345.79 tons/km²/yr, and the sediment accumulation rate is approximately 4.75 Mm³/yr. The dam’s half-life is estimated to be around 2076, but future precipitation projections may extend this timeframe due to the strong correlation between siltation and precipitation. Additionally, soil erosion driven by land management practices plays a crucial role in future siltation dynamics. Hence, this study offers a comprehensive assessment of the siltation dynamics at the Ahmed El Hansali dam, providing essential information on the long-term effects of erosion, land use changes, and climate projections. These findings may assist decision makers in managing dam reservoir sedimentation more effectively, ensuring the durability of the dam and extending the reservoir life.

This study aims to determine the behaviour of iron and its relation to the physicochemical properties in the hydromorphic soils of the Tadla plain (Morocco). An extensive analysis using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) from a statistical perspective was employed to ensure a comprehensive examination. The results reveal that the organic matter (OM) shows very high values, likely due to the collected samples near the sewerage network. Magnetic susceptibility (MS) measurements indicate that all the samples have positive and low values, indicating an abundance of paramagnetic minerals (such as ilmenite, siderite, and clay minerals). The iron concentration [ppm] increases from the top to the bottom of the Rivers, suggesting migration in a reduced form. Pearson’s correlation coefficients indicate that OM is positively correlated with cation exchange capacity (CEC) ( r = 0.83) and electrical conductivity (EC) ( r = 0.85) but negatively correlated with MS ( r = –0.57) and iron oxide ( r = 0.42). Calcium carbonate content (CaCO ₃ ) is positively correlated with sand ( r = 0.49), but negatively with MS ( r = –0.7). Iron (Fe) is positively correlated with frequency-dependent (FD) ( r = 0.7), but negatively with sand ( r = –0.608). Clay is strongly negatively correlated with silt ( r = –0.88) and oxalate extractable iron oxides (Feo) ( r = –0.55), respectively. These findings suggest that the physicochemical features of Beni Moussa hydromorphic soils are strongly linked and that iron in the soil is required for the stability of specific soil components.

Accurate and efficient streamflow simulations are necessary for sustainable water management and conservation in arid and semi-arid contexts. Conceptual hydrological models often underperform in these catchments due to the high climatic variability and data scarcity, leading to unstable parameters and biased results. This study evaluates the stability of the HBV model across seven sub-catchments of the Oum Er Rabia river basin (OERB), focusing on the HBV model regionalization process and the effectiveness of Earth Observation data in enhancing predictive capability. Therefore, we developed a nested cross-validation framework for spatiotemporal stability assessment, using optimal parameters from a donor-single-site calibration (DSSC) to inform target-multi-site calibration (TMSC). The results show that the HBV model remains spatially transferable from one basin to another with moderate to high performances (KGE (0.1~0.9 NSE (0.5~0.8)). Furthermore, calibration using KGE improves model stability over NSE. Some parameter sets exhibit spatial instability, but inter-annual parameter behavior remains stable, indicating potential climate change impacts. Model performance declines over time (18–124%) with increasing dryness. As a conclusion, this study presents a framework for analyzing parameter stability in hydrological models and highlights the need for more research on spatial and temporal factors affecting hydrological response variability.