Modeling of land use and land cover (LULC) is a very important tool, particularly in the agricultural field: it allows us to know the potential changes in land area in the future and to consider developments in order to prevent probable risks. The idea is to give a representation of probable future situations based on certain assumptions. The objective of this study is to make future predictions in land use and land cover in the watershed “9 April 1947”, and in the years 2028, 2038 and 2050. Then, the maps obtained with the climate predictions will be integrated into an agro-hydrological model to know the water yield, the sediment yield and the water balance of the studied area by 2050.The future land use and land cover (LULC) scenarios were created using a CA-Markov forecasting model. The results of the simulation of the LULC changes were considered satisfactory, as shown by the values obtained from the kappa indices for agreement (κstandard) = 0.73, kappa for lack of information (κno) = 0.76, and kappa for location at grid cell level (κlocation) = 0.80. Future scenarios modeled in LULC indicate a decrease in agricultural areas and wetlands, both of which can be seen as a warning of crop loss. There is, on the other hand, an increase in forest areas that could be an advantage for the biodiversity of the fauna and flora in the “9 April 1947” watershed.

This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area and “0” a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions.

Soil erosion is a serious ecological problem in Mediterranean areas. The IntErO model based on the erosion potential method (EPM) and the modified universal soil loss equation (MUSLE) have been used to assess soil erosion in several basins. This study aimed to assess and evaluate the effectiveness of these methods for evaluating sediment production and deposition rates in the Arbaa Ayacha basin, Morocco, in order to estimate sediment fluxes on a catchment scale. Our findings suggest that the basin is strongly exposed to erosion owing to geological formations, slope and land use, with average losses of about 28.4 t ha⁻¹ year⁻¹. Erosion processes were evaluated at the erosion production (Eocene marly formations) and sedimentation zones (Quaternary terraces). The results of these models may be useful to address soil and water management in this region and to assess the impact of a river dam that will be built in the basin.

For many years, the application of mixed-effects modeling has received much attention for predicting scenarios in the fields of theoretical and applied sciences. In this study, a “new” Multilevel Linear Mixed-Effects (LME) model is proposed to analyze and predict multiply-nested and hierarchical data. Temperature and rainfall observation were carried out successively between 1979-2014 and 1984–2018; and the data input was organized on monthly basis for each year. Besides, a daily observation was made for “Dar Chaoui” zone of Northern Morocco. However, we chose in the first time a simple linear regression model, but the estimation has been just for fixed effects and ignoring the random effect. On the other hand, in multilevel linear mixed effects models, once the model has been formulated, methods are needed to estimate the model parameters. In this section, we first deal with the joint estimation of the fixed effects (β), random effects (ui) and then with estimation of the variance parameters (γ, ρ and σ2). The study revealed that the predicted values are very close to the real value. Besides, this model is capable of modelling the error, fixed and random parts of the sample. Moreover, in this range, the results showed that there is three standard deviations measures for fixed and random effects, also the variance measure, which demonstrate us a great prediction. In conclusion, this model gives a decisive precision of results that can be exploited in studies for forecast of water balance and/or soil erosion. These results can also be used to inhibit the risk of erosion with possible arrangements for the environment and human security.

Terraced agroecosystems (TAS)—apart from being an important cultural heritage element—are considered vital for sustainable water resource management and climate change adaptation measures. However, this traditional form of agriculture, with direct implications in food security at a local scale, has been suffering from abandonment or degradation worldwide. In light of this, the need to fully comprehend the complex linkage of their abandonment with different driving forces is essential. The identification of these dynamics makes possible an appropriate intervention with local initiatives and policies on a larger scale. Therefore, the main aim of this paper is to introduce a comprehensive multidisciplinary framework that maps the dynamics of the investigated TAS’s abandonment, by defining cause–effect relationships on a hydrogeological, ecological and social level, through tools from System Dynamics studies. This methodology is implemented in the case of Assaragh TAS, a traditional oasis agroecosystem in the Moroccan Anti-Atlas, characterized by data scarcity. Through field studies, interviews, questionnaires and freely accessible databases, the TAS’s abandonment, leading to a loss in agrobiodiversity, is linked to social rather than climatic drives. Additionally, measures that can counteract the phenomenon and strengthen the awareness of the risks associated with climate change and food security are proposed.