During the last century, a great deal of effort has been directed toward determining soil erosion rates using various methods under a wide range of climatic conditions, soil types, land uses, topography, and among others. Therefore, to better understand soil erosion studies in Morocco, a country with diverse physiography and climatic variations we undertook an analysis of national data of several soil erosion modeling and fingerprinting. The approach used for this research is a review of scientific articles, conference papers and thesis on soil erosion, focusing more on categorization of the different soil erosion models and other methods applied. The results reveal very interesting information as follows: (i) the distribution and frequency level of modeling and fingerprinting applications; the focus was on the north of the country: (Rif 32.89%, High Atlas 32.89%, Occidental Meseta 18.43% and Middle Atlas 10.53%), (ii) The (R) USLE models remain the most widely used models (51,32%) in Morocco, (iii) The support practice factor was severely lacking across the country, (iv) the highest erosion rate is concentrated in the Atlas and Rif mountains; and (v) a positive relationship between erosion rate and geological features, slope, climate, land use and cover, plus other environmental characteristics, as well as measurement and modeling conditions, and a negative relationship with the study areas size and scale. Even though the overall results show a high degree of variability, which cannot be explained by this combination of factors, but is at a minimum partly related to the experimental conditions. This overview research and database are designed to assist in the future assessment of soil erosion and to help define priorities for soil erosion research by providing a state of art for future focused and comprehensive analyses to address this issue of soil erosion in Morocco.

Suspended sediment concentration (SSC) significantly impacts water quality, aquatic ecosystems, and reservoir capacity, making accurate prediction vital for effective watershed management. Traditional empirical and physically based models often struggle to handle the complexities and non-linear dynamics of sediment transport. Machine learning (ML) techniques, with their ability to model non-linear relationships and process large datasets, offer a promising alternative. This study explores the application of ML models, including extra trees (ET), random forest (RF), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost) and their combination with genetic programming (GP), to predict SSC. Key environmental variables such as precipitation, streamflow, and seasonality are used as inputs, and the models are trained and validated using historical hydrological data. The SHapley Additive exPlanations (SHAP) framework is employed to interpret the models, offering insights into the influence of each input variable on SSC predictions. Results demonstrate that ML models outperform traditional approaches in accuracy and robustness, particularly in capturing peak sediment events. The findings underline the potential of ML in improving SSC prediction and guiding sustainable watershed management practices.Keywords: Suspended Sediment Concentration (SSC), Machine Learning (ML), SHAP Values, Hydrological Modeling, Sediment Transport, Watershed Management.

Soil erosion presents a critical environmental challenge, particularly in regions exposed to climatic variability and anthropogenic pressures. In Morocco, where complex physiographic and climatic conditions prevail, the assessment of soil erosion and sediment transport is hindered by persistent data scarcity, limiting model accuracy and the effectiveness of watershed management. This thesis focuses on the development and application of robust soil erosion and sediment modeling approaches, combining both statistical and process-based methods.The study begins with a national-scale review of soil erosion assessments in Morocco. Results indicate that research is predominantly concentrated in the Rif and Atlas Mountains, with models based on the Revised Universal Soil Loss Equation (RUSLE) or its original version, the Universal Soil Loss Equation (USLE), representing over 51% of applications. However, many studies omit key parameters such as the support practice factor and have limited spatial coverage. Erosion rates were found to be strongly influenced by geomorphological, climatic, and land use factors, although methodological inconsistencies and data limitations contribute to significant variability.To address these challenges, an advanced modeling framework was applied in the Bouregreg watershed, a semi-arid basin in northwestern Morocco, with a focus on modeling suspended sediment concentration (SSC) and sediment yield (SY) over the period 09/01/2016–08/31/2021.The initial approach employed four machine learning (ML) algorithms, Extra Trees, Random Forest, CatBoost, and XGBoost, were integrated with Genetic Programming to enhance predictive accuracy and robustness. Model evaluation using Root Mean Square Error (RMSE), correlation coefficient (r), and Nash–Sutcliffe Efficiency (NSE) demonstrated strong performance (NSE: 0.53–0.86; RMSE: 1.20–2.55 g/L; r: 0.83–0.91) in predicting SSC. To improve interpretability, SHapley Additive exPlanations (SHAP) analysis was employed, revealing streamflow and seasonality as the most influential predictors.Subsequently, the RUSLE and the Modified Universal Soil Loss Equation (MUSLE) models were used to estimate soil loss and sediment yield using high-resolution soil data from INRA (1:50,000) and global FAO data (1:1,000,000). RUSLEINRA yielded more accurate results (15.56 t/ha/yr) than RUSLEFAO (10.24 t/ha/yr), while MUSLEINRA estimated 11.40 t/ha/yr. Sediment yield was validated using observed data from the Sidi Mohamed Ben Abdellah (SMBA) Dam. Projections under the SSP126 and SSP585 climate scenarios for the period 09/01/2021–12/31/2040 predict increased soil erosion (31.54–37.04 t/ha/yr), highlighting the urgent need for proactive soil conservation strategies.This thesis contributes a scalable and interpretable modeling framework that integrates machine learning and geospatial data for improved erosion prediction and watershed management under current and future climate conditions.