The Multi-Parameter Mapping of Groundwater Quality in the Bourgogne-Franche-Comté Region (France) for Spatially Based Monitoring Management

International audience

Английский. Groundwater, a vital resource for providing drinking water to populations, must be managed sustainably to ensure its availability and quality. This study aims to assess the groundwater quality in the Bourgogne-Franche-Comté region (~50,000 km2) of France and identify the processes responsible for its variability. Data were extracted from the Sise-Eaux database, resulting in an initial sparse matrix comprising 8723 samples and over 100 bacteriological and physicochemical parameters. From this, a refined full matrix of 3569 samples and 22 key parameters was selected. The data underwent logarithmic transformation before applying principal component analysis (PCA) to reduce the dimensionality of the dataset. The analysis of the spatial structure, using both raw and directional variograms, revealed a categorization of parameters, grouping major ions according to the regional lithology. Bacteriological criteria (Escherichia coli and Enterococcus) displayed strong spatial variability over short distances, whereas iron (Fe) and nitrates showed intermediate spatial characteristics between bacteriology and major ions. The PCA allowed the creation of synthetic maps, with the first seven capturing 80% of the information contained in the database, effectively replacing the individual parameter maps. These synthetic maps highlighted the different processes driving the spatial variations in each quality criterion. On a regional scale, the variations in fecal contamination were found to be multifactorial, with significant influences captured by the first four principal components. The 22 parameters can be grouped into six categories based on their spatial and temporal variations, allowing for the redefinition of a resource management and monitoring strategy that is adapted to the identified spatial patterns and processes at the regional scale, while also reducing analytical costs.