Along with being a dynamic process that affects large areas, desertification is also one of the most serious problems in many countries. The effects of this phenomenon threaten the sustainability of natural resources, namely water resources, agricultural production and major basic infrastructure, specifically roads and habitations. Several factors exacerbate this phenomenon such as the climate dryness, the geological and morphological characteristics of the terrain, the irrational use of space, population growth and the over-exploitation of vegetation and water resources. This work aims to evaluate the desertification index in the Oued-El-Maleh watershed, through the integration of key factors involved in the MEDALUS model (Mediterranean Desertification and Land Use) within a GIS. The model includes among its indexes: climate, vegetation, soil and management. Each index was obtained by the combination of sub-indexes. All the factors, measured and integrated into a geographic information system, enabled us to spatialize, on a synthetic map, the degree of the desertification effect throughout the watershed. This map is a managing tool available for decision-making regarding the selection of priority areas in the fight against desertification. High sensitivity to desertification class represents only 35% of the watershed. This class is concentrated in the north of the study area that corresponds to plains and low altitude. This could be explained by the dominance of agro-pastoral activity and the presence of a big population pressure.

Erosion is the main threat to sustainable water and soil management in Morocco. Located in the Souss-Massa watershed, the rural municipality of El Faid remains an area where gully erosion is a major factor involved in soil degradation and flooding. The aim of this study is to predict the spatial distribution of gully erosion at the scale of this municipality and to evaluate the predictive capacity of three prediction methods (frequency ratio (FR), logistic regression (LR), and random forest (RF)) for the characterization of gullying vulnerability. Twelve predisposing factors underlying gully formation were considered and mapped (elevation, slope, aspect, plane curvature, slope length (SL), stream power index (SPI), composite topographic index (CTI), land use, topographic wetness index (TWI), normalized difference vegetation index (NDVI), lithology, and vegetation cover (C factor). Furthermore, 894 gullies were digitized using high-resolution imagery. Seventy-five percent of the gullies were randomly selected and used as a training dataset, whereas the remaining 25% were used for validation purposes. The prediction accuracy was evaluated using area under the curve (AUC). Results showed that the factor that most contributed to the prevalence of gullying was topographic (slope, CTI, LS). Furthermore, the fitted models revealed that the RF model had a better prediction quality, with the best AUC (91.49%). The produced maps represent a valuable tool for sustainable management, land conservation, and protecting human lives against natural hazards (floods).

Climate change, which is expected to continue in the future, is increasingly becoming a major concern affecting many components of the biodiversity and human society. Understanding its impacts on forest ecosystems is essential for undertaking long-term management and conservation strategies. This study was focused on modeling the potential distribution of Quercus suber in the Maamora Forest, the world’s largest lowland cork oak forest, under actual and future climate conditions and identifying the environmental factors associated with this distribution. Maximum Entropy approach was used to train a Species Distribution Model and future predictions were based on different greenhouse gas emission scenarios (Representative Concentration Pathway RCPs). The results showed that the trained model was highly reliable and reflected the actual and future distributions of Maamora’s cork oak. It showed that the precipitation of the coldest and wettest quarter and the annual temperature range are the environmental factors that provide the most useful information for Q. suber distribution in the study area. The computed results of cork oak’s habitat suitability showed that predicted suitable areas are site-specific and seem to be highly dependent on climate change. The predicted changes are significant and expected to vary (decline of habitat suitability) in the future under the different emissions pathways. It indicates that climate change may reduce the suitable area for Q. suber under all the climate scenarios and the severity of projected impacts is closely linked to the magnitude of the climate change. The percent variation in habitat suitability indicates negative values for all the scenarios, ranging –23% to –100%. These regressions are projected to be more important under pessimist scenario RCP8.5. Given these results, we recommend including the future climate scenarios in the existing management strategies and highlight the usefulness of the produced predictive suitability maps under actual and future climate for the protection of this sensitive forest and its key species – cork oak, as well as for other forest species.

To assess the degree to which it has met its commitments under the Paris Agreement, Morocco is called upon to carry out carbon assessments and transparent evaluations. Within the forestry sector, little is known about the role of Morocco’s forests in contributing to carbon uptake. With this aim, we applied for the first time in the literature the 3-PG model to Cedrus atlantica ((Endl.) Manetti ex Carrière, 1855), which represents about 131,800 ha of Morocco’s forest area (i.e., Azrou forest). Through the Differential Evolution-Markov Chains (DE-MC) we tested and assessed the sensitivity and calibrated the 3-PG model. This process-based model provided significant results regarding the carbon sequestration capacity. The results showed the following: i. Parameters related to stand properties, canopy structure, and processes, as well as biomass partitioning, are the most important or sensitive for the performance of the model; ii. The DE-MC method optimized the values of the 3-PG parameters which was confirmed by the means of the Gelman–Rubin convergence test; iii. According to the predictions of the calibrated 3-PG, the Net Primary Production in the pure Azrou forest varies between 0.35 and 8.82 $\mathrm{tC}.{\mathrm{ha}}^{-1}.{\mathrm{yr}}^{-1}$, it is equal in average to 5.48 $\mathrm{tC}.{\mathrm{ha}}^{-1}.{\mathrm{yr}}^{-1}$, which given the total area corresponds to 7918 $\mathrm{tC}.{\mathrm{ha}}^{-1}$.

Factors determining forest species distribution include, in addition to external factors such as human interference and environmental management strategies, also soil and hydrological characteristics and climate conditions in any given areas. Modelling distribution has practical application in forest conservation and management, and help decision makers to develop strategies aimed at forest restoration, development of mountainous areas and the continuous and sustainable provision of forest-related services. Species distribution modelling (SDM) can be used for predicting species distribution based on tree presence records and on a number of environmental predictors. In this study we used MaxEnt for niche modelling in predicting carob (Ceratonia siliqua L.) trees spatial distribution in the Province of Azilal in Morocco. The results obtained show that a large area of the mountain regions is suitable for the expansion of Ceratonia siliqua stands. These findings will help decision makers in forest planning to better identify suitable sites for carob tree plantations and assess the potential of the exiting populations.