Bousouis A., Ayach M., El Jarjini Y., Mohsine I., Ravung L., Chakiri S., Bouabdli A., Valles V., Barbiéro Laurent. (2025). Mapping and assessing groundwater quality in Bourgogne-Franche-Comté (France) : toward optimized monitoring and management of groundwater resource. Water, 17 (9), p. 1396 [20 p.].
Titre du document
Mapping and assessing groundwater quality in Bourgogne-Franche-Comté (France) : toward optimized monitoring and management of groundwater resource
Bousouis A., Ayach M., El Jarjini Y., Mohsine I., Ravung L., Chakiri S., Bouabdli A., Valles V., Barbiéro Laurent
Source
Water, 2025,
17 (9), p. 1396 [20 p.]
To optimize the management of groundwater resources in the Bourgogne-Franche-Comté (BFC, France) region, data from the Size-Eaux database were cross-referenced with the French Reference Framework for Groundwater Bodies (GWB). The information contained in this dataset was synthesized using Principal Component Analysis (PCA), followed by Agglomerative Hierarchical Clustering (AHC) of GWBs based on their average coordinates along the main factorial axes. The results reveal 11 distinct GWB groups, each internally homogeneous in terms of chemical composition and ongoing processes responsible for intra-group variability. The distribution of the groups aligns with the region's structural geology, lithology, and agricultural activity patterns. Livestock farming areas, prone to fecal contamination, and cereal-growing areas, characterized by high nitrate concentrations, stand out distinctly. Furthermore, the analysis of GWB groups highlights regional processes such as denitrification, confirming the existence of spatial structuring of these mechanisms beyond local specificities. The major physicochemical and bacteriological zones show strong contrasts between groups while maintaining significant internal homogeneity. Despite the region's vast size and diversity, spanning three major watersheds, further subdivision was not necessary to obtain applicable results. These findings confirm observations made in other regions and pave the way for an optimized monitoring and surveillance strategy.
