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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 σ 2 ). 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.

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.

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