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.

The Kanyosha watershed is unstable due to the presence of several landslides, which occupy about 3% of the study area. They are causing major damage which costs expensive to the Government of Burundi as well as to the population residing there and their properties. Roads, schools, irrigation canals, houses, crop fields, etc., are in danger of collapse. These landslides are mostly naturally occurring but can sometimes be reactivated by heavy rains or human activities during the excavation of building materials from the river bed.In order to carry out this study, we used the multivariate statistical classification with weighting of the responsible parameters of landslides risk to reach the susceptibility map of mass movements in the Kanyosha watershed. Remote sensing, geology, morphometry and bibliography were the data sources for the different parameters. Google Earth images, ortho-photos and field prospecting helped us to identify the landslides needed to validate the susceptibility map.During the fieldwork, we observed 34 landslides of different types, which were superimposed on the mass movements susceptibility map obtained using the Analytic Hierarchy Process (AHP) and compared to previous studies in which the matrix indexing method was used. We found approximately similar results with the consideration of different scales of work. These reasons confirm the validity of the susceptibility map at the level of the Kanyosha watershed, a map which is an essential document for urban planning and land management.

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.

In France, the data resulting from monitoring water intended for human consumption are integrated into a national database called SISE-Eaux, a useful and relevant tool for studying the quality of raw and distributed water. A previous study carried out on all the data from the Provence-Alpes-Côte d’Azur (PACA) region in south-eastern France (1061 sampling points, 5295 analyses and 15 parameters) revealed that the dilution of the information in a heterogeneous environment constitutes an obstacle to the analysis of ongoing processes that are sources of variability. In this article, cross-referencing this information with the compartmentalization into groundwater bodies (MESO) provides a hydrogeological constraint on the dataset that can help to better define more homogeneous subsets and improve the interpretation. The approach involves three steps: (1) A principal component analysis conducted on the whole dataset aimed at eliminating information redundancy; (2) an unsupervised grouping of groundwater bodies having similar sources of variability; (3) a principal component analysis carried out within the main groups and sub-groups identified, aiming to define and prioritize the sources of variability and the associated processes. The results supported by discriminant analysis and machine learning show that the grouping of MESO is the best-suited scale to study ongoing processes due to greater homogeneity. One of the eight main groups identified in PACA, corresponding to the accompanying aquifers of the main rivers, is analyzed by way of illustration. Water–rock interactions, redox processes and their effects on the release of metals, arsenic and fecal contamination along different pathways were specifically identified with varying impacts according to the subgroups. We discussed both the significance of the principal components and the mean values of the bacteriological parameters, which provide information on the causes and on the state of contamination, respectively. Based on the results from two different groups of MESO, some guidelines in terms of a strategy for resource quality monitoring are proposed.

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.