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 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.

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.

The main landfill in the city of Rabat (Morocco) is based on sandy material containing the shallow Mio-Pliocene aquifer. The presence of a pollution plume is likely, but its extent is not known. Measurements of spontaneous potential (SP) from the soil surface were cross-referenced with direct measurements of the water table and leachates (pH, redox potential, electrical conductivity) according to the available accesses, as well as with an analysis of the landscape and the water table flows. With a few precautions during data acquisition on this resistive terrain, the results made it possible to separate the electrokinetic (~30%) and electrochemical (~70%) components responsible for the range of potentials observed (70 mV). The plume is detected in the hydrogeological downstream of the discharge, but is captured by the natural drainage network and does not extend further under the hills.

Une partie de l’alimentation en eau potable de Ouagadougou provient d’environ 1 800 puits traditionnels et 100 forages plus profonds de 40 à 65 m en moyenne. Une étude hydrochimique a été menée pour mieux connaître la qualité de la ressource en eau captée. Ses résultats permettent de proposer un modèle conceptuel du fonctionnement des aquifères dans la région de Ouagadougou, en contexte de socle cristallin. La ville connaît un climat de type soudano-sahélien avec une saison des pluies de juin à septembre et une saison sèche d’octobre à mai. Les précipitations moyennes sont de l’ordre de 700 mm. La géologie du secteur d’étude est constituée de formations de socle cristallin comportant une épaisse couverture d’altérites de 30 m environ. On y distingue, d’une part, l’aquifère supérieur libre de la cuirasse latéritique, capté par les puits et, d’autre part, isolé à son toit par des altérites argileuses, l’aquifère inférieur, souvent captif, constitué par les arènes grenues et le socle cristallin fissuré ou fracturé sous-jacent. Ce second aquifère est capté par les forages. L’eau des forages profonds présente un faciès bicarbonaté calcique et magnésien alors que celle captée par les puits présente un faciès chloruré et nitraté à tendance sodique. Le processus dominant dans l’aquifère inférieur est l’hydrolyse des silicates, alors que l’aquifère supérieur est fortement influencé par des apports superficiels. Dans ce cas, la minéralisation de la matière organique anthropique favorise la dissolution des silicates. Deux paramètres facilement mesurables sur le terrain, la conductivité électrique et l’alcalinité de l’eau, permettent de juger de ce phénomène. Les teneurs en nitrates dépassent les normes de l’OMS dans près de 60 % des 1 800 puits répertoriés. Le traitement statistique des concentrations mesurées en fonction de la distance séparant les différents ouvrages met en évidence l’hétérogénéité du milieu et le caractère localisé de l’influence des apports anthropiques. Cela confirme le concept d’une hydrodynamique en milieu discontinu caractéristique des zones de socle. Les variations saisonnières des teneurs en nitrates dans les puits indiquent un phénomène de pluvio-lessivage des sols durant la saison humide. Les teneurs en isotopes stables de l’eau (¹⁸O/²H) permettent de distinguer les puits, marqués par des phénomènes d’évaporation, des forages, dont les teneurs sont proches des teneurs moyennes pluriannuelles des pluies de pleine mousson. Ces observations valident un processus de recharge hétérogène et sélective. Elles indiquent deux types de recharge : l’une s’effectue directement à travers le manteau d’altération et l’autre de manière retardée à partir de mares et marigots soumis à des phénomènes d’évaporation.

Drainage network patterns influence the hydrological response of the watersheds and must be taken into account in the management of the water resource. In this context, it is important to identify the factors that control the configuration of drainage networks in and beyond specific climatic conditions. Here, we study 318 basins spread over three sectors (arid, semi-arid, and semi-humid) of Morocco where seven drainage network patterns have been identified. From each basin, 14 parameters were extracted, describing the relief, geology, morphometry, drainage network, land cover, precipitation, and time of concentration (Tc). Principal component analysis (PCA) and discriminant analysis (DA) processing were performed on the entire database and on each sector separately. The results show that the drainage network pattern is a feature of the landscape that contributes significantly to the variance of the basins. They suggest that the distribution of network patterns is controlled by the relationship between the different parameters, mainly those related to the relief, more than by the variations of each parameter taken individually. The network discrimination rate is 63.8%, which improves when each sector is treated separately. Confusion in discrimination are similar across all sectors and can be explained by similar conditions (active tectonic, deformation, and uplift) or transitions from one network pattern to another, due to the landscape evolution of certain sectors. A contribution of climatic variables appears locally but was attributed to a statistical coincidence, these parameters presenting a distribution close to that of the relief and geology variables.