Assessment of performance of the regional climate model (RegCM4.6) to simulate winter rainfall in the north of Morocco: The case of Tangier-Tétouan-Al-Hociema Region.

We examine the performance of the regional climate model RegCM v4.6 to simulate spatial variability of precipitation in the northwestern region of Morocco during the winter of 2009-2010. Simulations cover 24 months from 2009 to 2010 with 30 km as a horizontal grid. We use NCEP reanalysis as forcing data and for better comparison of results, observed precipitations derived from CRU, CHIRPS, and CMORPH data. Results indicate that, on the whole, the RegCM4 model represents appropriate regional aspects of rainfall over the study area but underestimates precipitations over mountainous and Mediterranean regions of the study area (Case of Tangier-Tétouan-Al-Hociema Region) which is probably due to poor representation of orography in the Model and some aspects of local Mediterranean climate. Projected precipitations are also examined in this work in comparison with the reference period of 1970-2005, with simulations performed by RegCM 4.6 regional model for the period 2023-2099 under scenarios RCP4.5 and RCP8.5, forced by HadGEM2-ES General Circulation Model. Results show a decrease in precipitations mean for (2023-2099) for both RCP4.5 and RCP8.5 scenarios over the study area in comparison with the historical period (1970-2005), with a significant decrease under RCP8.5 scenarios. This work proves that the RegCM v4.6 model can be used for regional climate prediction, particularly for the spatial distribution of precipitation, but for sectorial applications and impact studies, the Model outputs should be bias corrected.

Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R software.

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 (ß), 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.