Use of geomatics, Simulating the Impact of Future Land Use and Climate Change on Soil Erosion in the Tigrigra watershed (Azrou region, Middle Atlas, Morocco)

Soil losses need to be quantified in watersheds to implement erosion protection measures. The main objective of this work is to quantify soil loss in the Tigrigra watershed over the reference period 1985-2020 and two future periods 2050-2070, A Revised Universal Soil Loss Equation (RUSLE) model supported by geographic information systems (GIS) and remote sensing was used. GIS’s model generator can automate various operations of creating thematic layers of model parameters. For future climatic periods (2050-2070), precipitation was produced using a classical statistical downscaling model (SDSM). On the other hand, Automata/Markov models (CA Markov) are used to characterize future land use through modeling in Idrisi software. Over the two periods, the results showed that annual erosivity varies decreases, or increases. The annual soil loss maps showed that 50% of our study area was in the very low class (80 t/ha/year). These fluctuations are primarily due to the effects of climate change and deforestation/reforestation in the region. This leads to changes in soil erosion due to the important role played by these two factors

Use of geomatics and multi-criteria methods to assess water erosion in the Tigrigra watershed (Azrou region, Morocco)

In Morocco, the capacity of dam reservoirs has decreased in recent years due to water erosion. This study aims to identify the sub-watersheds most vulnerable to soil erosion in the Tigrigra watershed by utilizing morphometric analysis of linear, landscape, and shape parameters and various multi-criteria decision models. These approaches allow for the prioritization of areas or sub-watersheds at high erosion risk. In the study area, erosion assessment is conducted using multi-criteria decision support models (MCDM) such as MOORA, VIKOR, TOPSIS, COPRAS, WASPAS, and SAW within a GIS environment. This approach highlights the significant role of morphometric parameters and multi-criteria methods in identifying sub-watersheds susceptible to erosion. Overall, the results indicate that morphometric parameters are highly effective in identifying erosion-prone areas. The Tigrigra watershed generally exhibits low to medium sensitivity to erosion, except for certain sub-watersheds. Subcatchment 28 showed significant erosion in most methods used

Assessment of physicochemical and microbiological quality using the SEQ-Eau approach for groundwater in the Saïss basin (Fez-Meknes region, Morocco)

The Saïss water table is one of Morocco's major agricultural regions. Its water resources satisfy domestic, agricultural, industrial, and tourist needs. The present work focuses on the technique used to detect spatiotemporal variations in the overall physicochemical, microbiological, and heavy metal quality of groundwater in the Saïss basin, as assessed by the SEQ-Eau water quality system. A total of 28 samples were collected during high and low water periods, respectively. The results show that 25% of the stations present average quality during the dry season, and are located mainly in the southern part of the Meknes plateau in the El Hajeb, Boufekrane, and Agouray regions, while this pollution is reduced during the wet season with a percentage of 7.14%. However, the poor quality of groundwater indicates that 75% and 92.85% occupy almost the entire rest of the basin during the dry and wet seasons. Mapping of nitrate pollution of groundwater indicates that the lowest nitrate concentrations were recorded in the southwest part of the aquifer. The highest values were recorded in the center of the study area, with a maximum value of 118 mg/l, which exceeds the Moroccan standard due to the anthropogenic impact of agriculture and water use

Predicting Nitrate Levels in the Saïss Water Table: A Comparative Study of Machine Learning Methods

The main goal of this study is to predict nitrate (NO3-) levels in the Saiss basin water table as a function of various physicochemical parameters. To accomplish this, three machine learning approaches were utilized: multiple linear regression (MLR), super vector regression (SVR), and artificial neural networks (ANN). The independent variables were composed of six water quality parameters, including Ca2+, Na2+, EC, Cl-, HCO3-, and SO42-. The study utilized a dataset of 389 water samples collected between 1991 and 2017. The artificial neural network (ANN) was trained using the Levenberg-Marquardt (LM) algorithm, which was selected from various optimization algorithms. Additionally, during the training of the SVR model, it was observed that the RBF kernel outperformed the other kernels (linear, polynomial, and sigmoid kernel). The results were analyzed by the coefficient of determination (R2) and the mean square error (MSE). The results of the MLR method revealed R2 (0.523) and MSE (757.34). The ANN model with architecture [6-20-1] performed better than RLM with R2 = 0.836, MSE= 0.023 The SVR model result confirms what has been proved by ANN concerning the performance, with R2=0.902 and MSE= 4,364

Assessment of the risk of soil erosion using RUSLE method and SWAT model at the M’dez Watershed, Middle Atlas, Morocco

The preservation of soil resources is a primary global concern and a permanent challenge for all Mediterranean countries. In Morocco, the capacity of dam reservoirs continues to decline from one year to the next due to the rate of siltation, mainly due to the phenomenon of water erosion. Indeed, the origins of this erosion are generally related to land use planning, deforestation, agricultural practices and low vegetation cover. However, it is imperative to quantify soil erosion and its spatial distribution to achieve sustainable land use and governance of this resource. The SWAT hydro-agricultural model and the integrated RUSLE model were used to assess soil losses and characterize the degraded areas of the M’dez watershed, located in the upper Sebou, north of the Middle Atlas, and extend on an area of 3350 km2. The results obtained during this work show that the average soil losses estimated by the two models are consistent. For the SWAT model, the specific degradation of the watershed is estimated at 3.95 t/ha/year, whereas for the RUSLE model, the average loss of the basin is estimated at 2.94 t/ha/year). However, the use of these two models (SWAT and RUSLE), for the assessment and characterization of degraded areas at the level of Moroccan watersheds, has become a much sought-after approach for good soil conservation management