Tarik Bahaj

The present study area is an alluvial plain consisting of fine clayey sand and coarse sand with mixed lithology. In September 2018, a water sampling campaign was carried out in 28 wells spread all over the plain. The interpretation of the results from the physicochemical analysis allows determination of the hydrogeochemical behaviour and quality of the groundwater sampled in this plain. The spatial distribution of physical and chemical parameters reveals a clear demarcation between the east and the west of the study area. Water samples from wells located in the western part of the plain show a high mineralisation with high electrical conductivity and total dissolved solids. Water samples from the southern and eastern parts show intermediate to low mineralisation. Graphical representations of chemical analysis in the Piper diagrams reveal that the groundwater in the study area can be classified into five families. The evaluation of water quality shows that concentration values of some ions like Na⁺, Ca²⁺, Mg²⁺, Cl⁻, HCO₃⁻ and SO₄²⁻ are generally high in the western part of the plain along the Rusizi River with very high values particularly observed in the Rusizi delta.

Defining homogeneous units to optimize the monitoring and management of groundwater is a key challenge for organizations responsible for the protection of water for human consumption. However, the number of groundwater bodies (GWBs) is too large for targeted monitoring and recommendations. This study, carried out in the Provence-Alpes-Côte d’Azur region of France, is based on the intersection of two databases, one grouping together the physicochemical and bacteriological analyses of water and the other delimiting the boundaries of groundwater bodies. The extracted dataset contains 8627 measurements from 1143 observation points distributed over 63 GWB. Data conditioning through logarithmic transformation, dimensional reduction through principal component analysis, and hierarchical classification allows the grouping of GWBs into 11 homogeneous clusters. The fractions of unexplained variance (FUV) and ANOVA R² were calculated to assess the performance of the method at each scale. For example, for the total dissolved load (TDS) parameter, the temporal variance was quantified at 0.36 and the clustering causes a loss of information with an R² going from 0.63 to 0.4 from the scale of the sampling point to that of the GWB cluster. The results show that the logarithmic transformation reduces the effect of outliers and improves the quality of the GWB clustering. The groups of GWBs are homogeneous and clearly distinguishable from each other. The results can be used to define specific management and protection strategies for each group. The study also highlights the need to take into account the temporal variability of groundwater quality when implementing monitoring and management programs.

Water plays an important role in power generation, fuel manufacturing, and processing. This has been valid for several decades, but lately, primarily due to climate change, the limitations and insecurity related to water energy connections have become more prominent. The article is a quantitative review study conducted to evaluate the water–energy nexus in the Middle East and North Africa (MENA) region. Information about the review was generated from online databases by using keywords such as water–energy nexus, MENA region, Power Generation, Fuel Manufacturing, Energy-intensive, Energy Management Decisions, and Desalination Systems. Drip irrigation in Morocco played a vital role in the water–energy nexus for resource conservation and their better utilization. From the findings, it was revealed that distorted coupling with a relatively low reliance on freshwater energy systems has a high reliance on conceptual water and energy production systems. For Saudi Arabia, extraction and desalination of groundwater are projected to be up to 9% of total annual electricity use. Policymakers should consider energy implications for water-intensive food imports and possible water demand restructuring. This would lead to more coordinated water and energy management decisions. A comprehensive evaluation in some cases promotes the reuse of water and improvements in the agricultural sector rather than the development of energy-intensive and expensive desalination systems. One of the limitations for water–energy nexus in the MENA region is its unintelligible patterns for policy and decision-makers, and this quantitative review can be a major advancement in this regard. This study also highlights the use of water as an energy production source as well as the energy that is being utilized in water treatment and processing and their interrelationship. Cohesive and strategic tactics can lead technology’s research and development to reporting local issues of water and energy issues. Improving and participating models and data will better assist scholars, decision-makers, and the community. This water–energy nexus study mounts relevant challenges and areas of improvement for future research.

Urban flooding is a complex natural hazard, driven by the interaction between several parameters related to urban development in a context of climate change, which makes it highly variable in space and time and challenging to predict. In this study, we apply a multivariate analysis method (PCA) and four machine learning algorithms to investigate and map the variability and vulnerability of urban floods in the city of Tangier, northern Morocco. Thirteen parameters that could potentially affect urban flooding were selected and divided into two categories: geo-environmental parameters and socio-economic parameters. PCA processing allowed identifying and classifying six principal components (PCs), totaling 73% of the initial information. The scores of the parameters on the PCs and the spatial distribution of the PCs allow to highlight the interconnection between the topographic properties and urban characteristics (population density and building density) as the main source of variability of flooding, followed by the relationship between the drainage (drainage density and distance to channels) and urban properties. All four machine learning algorithms show excellent performance in predicting urban flood vulnerability (ROC curve > 0.9). The Classifications and Regression Tree and Support Vector Machine models show the best prediction performance (ACC = 91.6%). Urban flood vulnerability maps highlight, on the one hand, low lands with a high drainage density and recent buildings, and on the other, higher, steep-sloping areas with old buildings and a high population density, as areas of high to very-high vulnerability.

The study area, in northwestern Burundi, is an alluvial plain consisting of fine clayey sands and coarse sands with mixed lithology. The aquifer of the lower Rusizi plain could be considered as confined under a clay layer. A 2D horizontal groundwater flow model was developed under steady-state conditions using the Modflow software. The study aims to determine the most productive areas of this confined alluvial aquifer and the main aquifer inflow and outflow values together with the recharge and river–aquifer interactions. The groundwater potential is dependent on the spatial distribution of hydraulic conductivity and aquifer thickness values providing the local transmissivity values. The calibrated model made it possible to assess the spatial distribution of the hydraulic conductivity values at the regional scale, which ranged from 6 × 10⁻⁶ (contact between alluvial plain and Precambrian basement) to 7.5 × 10⁻³ m/s (coastal barriers). The results also provided the computed groundwater flow directions, and an estimation of the groundwater levels in areas not yet investigated by drilling. The results of the computed groundwater flow budget allowed us to deduce that recharge and river–aquifer interaction constitute the main inflow while the downwards boundaries (where piezometric heads could be prescribed) are the main zones where outflows occur. The results of this model can be used in the planning of pumping test programs, locating areas with high groundwater potential to plan water supply for different private and public users. This predictive tool will contribute to the resolution of problems related to the use and integrated management of the groundwater resource in this part of Burundi.

The SISE-Eaux database of water intended for human consumption, archived by the French Regional Health Agency (ARS) since 1990, is a rich source of information. However, more or less regular monitoring over almost 30 years and the multiplication of parameters lead to a sparse matrix (observations × parameters) and a large dimension of the hyperspace of data. These characteristics make it difficult to exploit this database for a synthetic mapping of water quality, and to identify of the processes responsible for its diversity in a complex geological context and anthropized environment. A 10-year period (2006–2016) was selected from the Provence-Alpes- Côte d’Azur region database (PACA, southeastern France). We extracted 5,295 water samples, each with 15 parameters. A treatment by principal component analysis (PCA) followed with orthomax rotation allows for identifying and ranking six principal components (PCs) totaling 75% of the initial information. The association of the parameters with the principal components, and the regional distribution of the PCs make it possible to identify water-rock interactions, bacteriological contamination, redox processes and arsenic occurrence as the main sources of variability. However, the results also highlight a decrease of useful information, a constraint linked to the vast size and diversity of the study area. The development of a relevant tool for the protecting and managing of water resources will require identifying of subsets based on functional landscape units or the grouping of groundwater bodies.

Un vertedero de 110 ha, ubicado sobre el acuífero superficial de Akrach en Marruecos, ha sido estudiado para caracterizar la calidad de las aguas subterráneas alrededor del vertedero, conocer la extensión del área contaminada y estudiar los factores involucrados en esta contaminación. El vertedero se emplazó en la ciudad de Um Azza (15 km al sur de Rabat), entre el rio Akrach, al oeste, y la represa Sidi Mohaed Ben Abdallah, al este. Un control piezómetro y análisis químicos se hicieron sobre 22 pozos para estudiar esta contaminación. La calidad del agua es afectada por una contaminación importante, especialmente en las áreas al contacto cercano del vertedero, y aguas abajo de este mismo. En las zonas afectadas la conductividad eléctrica de las aguas supera 1100 µS.cm-1, los bicarbonatos superan 508 mg.L-1 , los cloruros, sulfatos y nitratos superan 850,200 y 1 mg.L-1. Además se consiguió alto contenido en cadmio (50-100 µg.L-1) y cromo (40-230 µg.L-1). En definitiva, se superaron olgadamente los valores estipulados en las normas de la OMS para el agua potable y de riego. Un análisis en componentes principales se llevó a cabo sobre el conjunto de datos. No aparece un eje factorial consistente, sino más bien muchos ejes de importancia similar, lo que muestra un conjunto de mecanismos de la misma fuerza involucrados en la calidad química de las aguas contaminadas. Una clasificación jerárquica ascendente da tres grupos de muestras, cada grupo corresponde a un nivel de contaminación.