Quantification de L’érosion Hydrique au Niveau du Bassin Versant à Lamont du Barrage Hassan II, Haute Moulouya, Maroc, par L’équation Universelle de Perte en Sol

Le bassin versant à l’amont du barrage Hassan II, situé au cœur de la Haute Moulouya, est de 3379 km² de superficie et de 512 km de périmètre. Le présent travail a pour objectif d’estimer les pertes en sol au niveau du bassin versant à l’amont du barrage Hassan II dans la Haute Moulouya par l’utilisation des systèmes d’Information Géographique (SIG). La méthodologie adoptée est basée sur une succession d’étapes permettant d’aboutir au modèle final (carte d’érosion) en utilisant le modèle de Wischmeier pour déterminer les zones susceptibles d’être érodées. Les résultats obtenus montrent que Le facteur d’érosivité R est en moyenne de 72,15 avec une valeur maximale de 99,16. Quant au facteur d’érodibilité des sols, il varie de 0,1 à 0,45 avec une moyenne de 0,27. Le facteur topographique LS varie de 0 à 322. Concernant le couvert végétal, On note une protection faible du sol à l’échelle du bassin du fait que la majorité du territoire est occupée par des terrains de cultures et que seulement 16.3% de la superficie du bassin versant se caractérise par une bonne protection de sol avec un facteur C inférieur à 0,01. L’étude a montré que le bassin versant est à 82,7% de superficie protégée contre l’érosion, celle-ci étant inférieure à 7 t/ha/an (le seuil de tolérance). La perte en sol moyenne du bassin est évaluée à 9,12 t/ha/an, avec 11,24% de la superficie totale du bassin. Cette valeur correspond à une érosion moyennement faible. La superficie soumise à de forte érosion de plus de 28 t/ha/an, soit 6,5% de la superficie totale, est en grande partie très accidentée à forte pente avec un réseau hydrographique dense. Par conséquent, la topographie et le réseau hydrographique constituent les principaux facteurs explicatifs de telles valeurs. Ces valeurs d’érosion doivent être prise avec prudence vu que le dit modèle ne calcule pas la sédimentation qui pourrait réduire énormément l’impact de l’érosion sur le sol.   The watershed upstream of the Hassan II dam, located in the heart of the Upper Moulouya, is 3379 km² in the area and 512 km in the perimeter. The objective of this work is to estimate the soil losses in the watershed upstream of the Hassan II Dam in the Upper Moulouya by using Geographic Information Systems (GIS). The methodology adopted is based on a succession of steps leading to the final model (erosion map) using the Wischmeier model to determine the areas likely to be eroded. The results obtained show that the erosion factor R is on average 72.15 with a maximum value of 99.16. The soil erodibility factor varies from 0.1 to 0.45 with an average of 0.27. The topographic factor LS varies from 0 to 322. Concerning the vegetation cover, we note weak protection of the soil at the scale of the basin because the majority of the territory is occupied by cultivated land and only 16.3% of the surface of the watershed is characterized by good protection of soil with a factor C lower than 0.01. The study showed that the watershed has 82.7% of the area protected against erosion, which is less than 7 t/ha/year (the tolerance threshold). The average soil loss of the basin is estimated at 9.12 t/ha/year, with 11.24% of the total area of the basin. This value corresponds to moderately low erosion. The area subject to high erosion of more than 28 t/ha/year, or 6.5% of the total area, is largely very hilly with a steep slope and a dense hydrographic network. Therefore, the topography and the hydrographic network are the main explanatory factors for such values. These erosion values should be taken with caution, as the model does not calculate sedimentation, which could greatly reduce the impact of erosion on the soil

Quantification de L’érosion Hydrique au Niveau du Bassin Versant à Lamont du Barrage Hassan II, Haute Moulouya, Maroc, par L’équation Universelle de Perte en Sol

Le bassin versant à l’amont du barrage Hassan II, situé au cœur de la Haute Moulouya, est de 3379 km² de superficie et de 512 km de périmètre. Le présent travail a pour objectif d’estimer les pertes en sol au niveau du bassin versant à l’amont du barrage Hassan II dans la Haute Moulouya par l’utilisation des systèmes d’Information Géographique (SIG). La méthodologie adoptée est basée sur une succession d’étapes permettant d’aboutir au modèle final (carte d’érosion) en utilisant le modèle de Wischmeier pour déterminer les zones susceptibles d’être érodées. Les résultats obtenus montrent que Le facteur d’érosivité R est en moyenne de 72,15 avec une valeur maximale de 99,16. Quant au facteur d’érodibilité des sols, il varie de 0,1 à 0,45 avec une moyenne de 0,27. Le facteur topographique LS varie de 0 à 322. Concernant le couvert végétal, On note une protection faible du sol à l’échelle du bassin du fait que la majorité du territoire est occupée par des terrains de cultures et que seulement 16.3% de la superficie du bassin versant se caractérise par une bonne protection de sol avec un facteur C inférieur à 0,01. L’étude a montré que le bassin versant est à 82,7% de superficie protégée contre l’érosion, celle-ci étant inférieure à 7 t/ha/an (le seuil de tolérance). La perte en sol moyenne du bassin est évaluée à 9,12 t/ha/an, avec 11,24% de la superficie totale du bassin. Cette valeur correspond à une érosion moyennement faible. La superficie soumise à de forte érosion de plus de 28 t/ha/an, soit 6,5% de la superficie totale, est en grande partie très accidentée à forte pente avec un réseau hydrographique dense. Par conséquent, la topographie et le réseau hydrographique constituent les principaux facteurs explicatifs de telles valeurs. Ces valeurs d’érosion doivent être prise avec prudence vu que le dit modèle ne calcule pas la sédimentation qui pourrait réduire énormément l’impact de l’érosion sur le sol.   The watershed upstream of the Hassan II dam, located in the heart of the Upper Moulouya, is 3379 km² in the area and 512 km in the perimeter. The objective of this work is to estimate the soil losses in the watershed upstream of the Hassan II Dam in the Upper Moulouya by using Geographic Information Systems (GIS). The methodology adopted is based on a succession of steps leading to the final model (erosion map) using the Wischmeier model to determine the areas likely to be eroded. The results obtained show that the erosion factor R is on average 72.15 with a maximum value of 99.16. The soil erodibility factor varies from 0.1 to 0.45 with an average of 0.27. The topographic factor LS varies from 0 to 322. Concerning the vegetation cover, we note weak protection of the soil at the scale of the basin because the majority of the territory is occupied by cultivated land and only 16.3% of the surface of the watershed is characterized by good protection of soil with a factor C lower than 0.01. The study showed that the watershed has 82.7% of the area protected against erosion, which is less than 7 t/ha/year (the tolerance threshold). The average soil loss of the basin is estimated at 9.12 t/ha/year, with 11.24% of the total area of the basin. This value corresponds to moderately low erosion. The area subject to high erosion of more than 28 t/ha/year, or 6.5% of the total area, is largely very hilly with a steep slope and a dense hydrographic network. Therefore, the topography and the hydrographic network are the main explanatory factors for such values. These erosion values should be taken with caution, as the model does not calculate sedimentation, which could greatly reduce the impact of erosion on the soil

Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System

Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid/ensemble machine learning (ML) models are developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector Machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, and DRASTIC-RF-MLP, for groundwater pollution assessment in the Saiss basin, in Morocco. The performances of these models are evaluated using the Receiver Operating Characteristic curve (ROC curve), precision, and accuracy. Based on the results of the ROC curve method, it is indicated that the use of hybrid/ensemble machine learning (ML) models improves the performance of the individual machine learning (ML) algorithms. In effect, the AUC value of the original DRASTIC is 0.51. Furthermore, both hybrid/ensemble models, DRASTIC-RF-MLP (AUC = 0.953) and DRASTIC-RF-SVM, (AUC = 0.901) achieve the best accuracy among the other models, followed by DRASTIC-RF (AUC = 0.852), DRASTIC-SVM (AUC = 0.802), and DRASTIC-MLP (AUC = 0.763). The results delineate areas vulnerable to pollution, which require urgent actions and strategies to improve the environmental and social qualities for the local population

Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System

Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid/ensemble machine learning (ML) models are developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector Machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, and DRASTIC-RF-MLP, for groundwater pollution assessment in the Saiss basin, in Morocco. The performances of these models are evaluated using the Receiver Operating Characteristic curve (ROC curve), precision, and accuracy. Based on the results of the ROC curve method, it is indicated that the use of hybrid/ensemble machine learning (ML) models improves the performance of the individual machine learning (ML) algorithms. In effect, the AUC value of the original DRASTIC is 0.51. Furthermore, both hybrid/ensemble models, DRASTIC-RF-MLP (AUC = 0.953) and DRASTIC-RF-SVM, (AUC = 0.901) achieve the best accuracy among the other models, followed by DRASTIC-RF (AUC = 0.852), DRASTIC-SVM (AUC = 0.802), and DRASTIC-MLP (AUC = 0.763). The results delineate areas vulnerable to pollution, which require urgent actions and strategies to improve the environmental and social qualities for the local population

Towards a Decision-Making Approach of Sustainable Water Resources Management Based on Hydrological Modeling: A Case Study in Central Morocco

Water is one of the fundamental resources of economic prosperity, food security, human habitats, and the driver of many global phenomena, such as droughts, floods, contaminated water, disease, poverty, and hunger. Therefore, its deterioration and its inadequate use lead to heavy impacts on environmental resources and humans. Thus, we argue that to address these challenges, one can rely on hydrological management strategies. The objective of this study is to simulate and quantify water balance components based on a hydrologic model with available data at the R’Dom watershed in Morocco. For this purpose, the hydrologic model used is the Soil and Water Assessment Tool + (SWAT+) model. The streamflow model simulations were run at the monthly time step (from 2002 to 2016), during the calibration period 2002–2009, the coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) values were 0.84 and 0.70, respectively, and 0.81 and 0.65, respectively, during the validation period 2010–2016. The results of the water balance modeling in the watershed during the validation period revealed that the average annual precipitation was about 484 mm, and out of this, 5.75 mm came from the development of irrigation in agricultural lands. The evapotranspiration accounted for about 72.28% of the input water of the watershed, while surface runoff (surq_gen) accounted for 12.04%, 11.90% was lost by lateral flow (latq), and 4.14% was lost by groundwater recharge (perco). Our approach is designed to capture a real image of a case study; zooming into other case studies with similar environments to uncover the situation of water resources is highly recommended. Moreover, the outcomes of this study will be helpful for policy and decision-makers, and it can be a good path for researchers for further directions based on the SWAT model to simulate water balance to achieve adequate management of water resources

Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale

Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of “where and why” gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area

Remote Sensing Data for Geological Mapping in the Saka Region in Northeast Morocco: An Integrated Approach

Together with geological survey data, satellite imagery provides useful information for geological mapping. In this context, the aim of this study is to map geological units of the Saka region, situated in the northeast part of Morocco based on Landsat Oli-8 and ASTER images. Specifically, this study aims to: (1) map the lithological facies of the Saka volcanic zone, (2) discriminate the different minerals using Landsat Oli-8 and ASTER imagery, and (3) validate the results with field observations and geological maps. To do so, in this study we used different techniques to achieve the above objectives including color composition (CC), band ratio (BR), minimum noise fraction (MNF), principal component analysis (PCA), and spectral angle mapper (SAM) classification. The results obtained show good discrimination between the different lithological facies, which is confirmed by the supervised classification of the images and validated by field missions and the geological map with a scale of 1/500,000. The classification results show that the study area is dominated by Basaltic rocks, followed by Trachy andesites then Hawaites. These rocks are encased by quaternary sedimentary rocks and an abundance of Quartz, Feldspar, Pyroxene, and Amphibole minerals