Search for a Relevant Scale to Optimize the Quality Monitoring of Groundwater Bodies in the Occitanie Region (France)

Abstract

In France, and more generally in Europe, the high number of groundwater bodies (GWB) per administrative region is an obstacle for the management and monitoring of water for human consumption by regional health agencies. Moreover, GWBs show a high spatial, temporal, physico-chemical, and bacteriological variability. The objective is to establish homogeneous groupings of GWB from the point of view of water quality and the processes responsible for this quality. In the Occitanie region in southwestern France, the cross-referencing of two databases, namely the French reference system for groundwater bodies and SISE-EAUX, provided a dataset of 8110 observations and 15 parameters distributed over 106 GWB. The 8-step approach, including data conditioning, dimensional reduction by Principal Component Analysis, and hierarchical clustering, resulted in 20 homogeneous groups of GWB over the whole region. The loss of information caused by this grouping is quantified by the evolution of the explained variance. Splitting the region into two large basins (Adour-Garonne and Rhône Méditerranée) according to the recommendations of the European community does not result in a significant additional loss of information contained in the data. A quick study of a few groups allows to highlight the specificities of each one, thus enabling targeted guidelines or recommendations for water quality management and monitoring. In the future, the method will have to be tested on the scale of large European watersheds, as well as in the context of an increase in the number of parameters.

Groundwaters in the Auvergne-Rhône-Alpes Region, France: Grouping Homogeneous Groundwater Bodies for Optimized Monitoring and Protection

“…The data used in this study were extracted from the Sise-Eaux database (https: //data.eaufrance.fr/concept/sise-eaux, accessed in 15 March 2021). For further details regarding this national database, previous works by our research group [8][9][10][11][12][13][14][15], as well as the basic references [25,26], can be consulted. The extraction conducted for a 30-year period from July 1990 to September 2020 for our study area resulted in a sparse matrix of 114,033 observations (water samples) distributed across 3146 sampling points (Figure 3a), with 21 measured parameters.…”

“…Moreover, it reflects the multitude of mechanisms contributing to water quality. The grouping of groundwater bodies (GWBs) into homogeneous sets should therefore help better discriminate these mechanisms [12]. The GWB groups derived from the Auvergne-Rhône-Alpes region primarily consist of geographically proximate GWBs, forming cohesive geographical clusters based on lithological attributes.…”

“…The presence of extreme values in the dataset exaggerated the impact of certain parameters, which was addressed by logarithmic data conditioning [10,11]. Discriminating spatial and temporal variance helped identify seasonal mechanisms or long-term trends [12,13]. A study conducted in the vast Auvergne-Rhône-Alpes region established a typology of quality parameters based on structures or associations between these parameters, differing in terms of spatial extent, seasonality, or long-term behavior [14].…”

“…This method works across regions of variable size, ranging from moderately contrasting (Provence-Alpes-Côte d'Azur) to larger but moderately contrasting in lithological, altitudinal, environmental, or land use aspects. Its application to the Occitanie region, situated between two watersheds facing the Atlantic (Adour-Garonne) and the Mediterranean (Rhône-Mediterranée), emphasized the need to split this region into two sub-units corresponding to the two major basins to limit information loss during the grouping of homogeneous groundwater bodies [12]. In this context, the objective of this work is twofold.…”

“…Only the pH, which already corresponds to the logarithmic transformation of the chemical activity of H 3 O + , was retained without conditioning. The goal was to align the distributions of each parameter with a normal distribution, but more importantly, to limit the influence of extreme values that could mask certain processes responsible for the variability in water quality within the dataset [11,12]. 2.…”