Groundwater, a vital resource for drinking water supply, must be managed sustainably to ensure its availability and quality. In France, the SISE-Eaux database on water intended for human consumption, archived by the Regional Health Agencies (ARS) since 1990, constitutes a rich source of information. This study focused on the groundwater of the Nouvelle-Aquitaine region, the largest administrative region in metropolitan France, covering 84,061 km² with 6 million inhabitants. It is based on a 22-year data extraction, resulting in a matrix of 121,649 observations and 51 physico-chemical and bacteriological parameters. Following logarithmic transformation of the data and fitting of variograms using the mean value of each parameter for each sampling point, the spatial distribution of numerous parameters across the region is presented. From this initial sparse matrix, a dense matrix of 23,319 samples (rows) and 15 key parameters (columns) was selected for a multivariate approach. A Principal Component Analysis (PCA) was used to condense the information and create summary maps capturing over 68% of the information contained in the dense matrix. The combined results of the multivariate analysis (dense matrix) and the distribution of individual parameters (sparse matrix) highlight the diversity of sources contributing to the spatial variability of groundwater, such as the role of lithology, the origin and pathways of fecal contamination, and the influence of redox processes. Neither the large size of the study area nor the high number of parameters proved to be an obstacle to the analysis. The understanding of ongoing processes and the factorial axis distribution maps, which enable the spatial representation of these mechanisms, can be used to facilitate groundwater monitoring and protection.

In the Rhône-Mediterranean-Corsica (RMC) basin (130,000 km², 14 million inhabitants), groundwater intended for human consumption has been monitored for decades. These data, stored in the SISE-EAUX database, were cross-referenced with information from the CORINE Land Cover (CLC) database, which describes human land use, in order to identify potential relationships between pollutant pressure and water quality at the basin scale, as well as the mechanisms specific to each geographical area. Data processing was carried out in three stages. The 27,741 water samples from 2825 abstraction points were assigned to the 224 groundwater bodies (GWBs), and average values for each physicochemical and bacteriological parameter were calculated for each GWB. At the same time, the percentage of surface area covered by each land use type was also extracted at the scale of each GWB. This information was subjected to statistical processing, first separately and then jointly, using principal component analysis (PCA) and hierarchical clustering of parameters. A redundancy in the information carried by the quality parameters, previously observed at the scale of administrative regions (four to five times smaller), is confirmed at this new analysis scale, paving the way for data consolidation and a more synthetic representation. Fecal contamination primarily concerns areas with crystalline lithology and, secondarily, a few karst sectors, with other livestock farming regions being less contaminated. Higher nitrate concentrations are observed in cereal-growing regions and areas of intensive row cropping, while metal concentrations are lower in the drier Mediterranean climate zone than under the more humid continental climate. Structuring factors, notably altitude and climate, emerge at the RMC basin analysis scale, which was not the case at the scale of administrative regions. These structuring factors influence land use, soil type, and hydrological regimes alike, which explains the correlations between the information contained in the CLC and SISE-EAUX databases.

This study analyses groundwater quality in the Rhône–Mediterranean–Corsica (RMC) basin using 27,741 samples collected from 7,625 sampling points and 224 groundwater bodies, covering a 34-year period (1987–2021). The objective is to quantify, using statistical and machine learning methods, the respective contributions of temporal variability, spatial variability, and lithology to the hydrochemical and microbiological composition of aquifers. The results reveal a marked dichotomy between microbiological and physico-chemical parameters. Faecal contamination (E. coli, enterococci) is dominated by a strong temporal component (33–36% of variance), whereas spatial structuring by lithology remains very limited (< 11%). Conversely, major physico-chemical parameters (calcium, conductivity, sulphates, etc.) exhibit near-zero temporal variability (<7%) and a spatial structuring strongly controlled by lithology, with gains in explained variance of up to 20% through lithological grouping. Linear discriminant analysis achieves an overall classification rate of 56.2%, but with wide disparities: the crystalline basement is perfectly discriminated (76%), while alluvial formations are not discriminated at all (0%), as their chemical signature is more strongly influenced by anthropogenic pressures than by lithology. Machine learning methods, particularly k-nearest neighbours (k-NN), significantly improve performance (76.2% vs. 56.2% for LDA), confirming the existence of nonlinear structures in the data. Unsupervised validation via K-means confirms the reality of the lithological signal. From an operational standpoint, these findings justify a differentiated approach to groundwater management: a geological approach for chemical parameters, and an approach based on land use and agropastoral practices for bacteriological parameters. The proposed methodology – combining PCA, HCA, nested ANOVA, LDA and machine learning – provides a transferable analytical framework applicable to other major European river basins.