Search

Morocco ranks among countries with the greatest achievements in the field of dams in Africa but is affected by the sedimentation phenomenon due to soil erosion in upstreams. The assessment of Sediment Yield (SY) and Suspended Sediment Yield (SSY) remains a challenging global issue, especially in Morocco, characterized by a great diversity of morphological, climatic, and vegetation cover. The main objective of this paper was to perform advanced statistics and artificial neural networks (ANN) in order to understand the spatial distribution of sediment yield and the factors most controlling it, including factors of the RUSLE model (Revised Universal Soil Loss Equation). In order to produce a model able to assess SY, we collected and analyzed extensive data of most variables that can be affecting SY using 42 catchments of the biggest and important dams of Morocco. Statistical analysis of the studied watersheds shows that SY is mainly related to the watershed area and the length of the drainage network.  On the other hand, the SSY is higher in watersheds where gully erosion is abundant and lower in areas with no soil horizon. The SSY is mainly related to the altitude, aridity index, sand fraction, and drainage network length. In front of the complexity of preserving this phenomenon, the ANN was applied and gave very good satisfactory results in predicting the SSY (NSE=0.93, R2=0.93).

In recent decades, the Bouregreg Chaouia region has been subject to urban growth and a reduction in agricultural land in this region, which has changed its environmental variables and made it vulnerable to climate change. This work raises the spatiotemporal monitoring of land use and certain environmental parameters (vegetation cover, albedo, surface temperature from 1987 to 2015 by exploring intelligent spatial data in the region. The remote sensing products were computed from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 Oli/TIRS images obtained during the dry seasons 1987, 2000 and 2015. The results showed a reduction in NDVI vegetation index (∼0.86 in 1987 to ∼0. 56 in 2000 to ∼ 0.54 in 2015) and with an increase in surface albedo (0.51 in 1987 to 0.52 in 2000 to 0. 69 in 2015), temperature (∼67°C in 1987 to 54°C in 2000 to 40°C in 2015) and to understand the impact of urbanization on the variation of environmental parameters, the evolution of the built-up area has been followed as a determining factor. However, it recorded 3.27% surface area in 1987 to 7.45% in 2000 to 28.18% in 2015. Indeed, the contribution of new technologies (GIS and remote sensing) is essential for better management and monitoring of the impact of urban expansion on the state of the environment. The results obtained remain so promising and highlight the contribution and feasibility of intelligent spatial data to assess the evolution of the urban environment on a large scale.