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

Author:

Ayach Meryem1,Lazar Hajar1,Lamat Christel2,Bousouis Abderrahim3,Touzani Meryem4,El Jarjini Youssouf5ORCID,Kacimi Ilias1ORCID,Valles Vincent67,Barbiero Laurent8ORCID,Morarech Moad5

Affiliation:

1. Geosciences, Water and Environment Laboratory, Faculty of Sciences Rabat, Mohammed V University, Rabat 10000, Morocco

2. Agence Régionale de Santé ARS Auvergne-Rhône-Alpes, 241 Rue Garibaldi, 69003 Lyon, France

3. Laboratoire de Géosciences, Faculté des Sciences, Université Ibn Tofaïl, BP 133, Kénitra 14000, Morocco

4. National Institute of Agronomic Research, Rabat 10060, Morocco

5. Laboratory in Applied and Marine Geosciences, Geotechnics and Georisk (LR3G), Faculty of Science Tetouan, Abdelmalek Essaâdi University, Tetouan 93002, Morocco

6. Mixed Research Unit EMMAH (Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes), Hydrogeology Laboratory, Avignon University, 84916 Avignon, France

7. Faculté des Sciences et Techniques (FSTBM), BP 523, Beni Mellal 23000, Morocco

8. Institut de Recherche pour le Développement, Géoscience Environnement Toulouse, CNRS, University of Toulouse, Observatoire Midi-Pyrénées, UMR 5563, 14 Avenue Edouard Belin, 31400 Toulouse, France

Abstract

The number and diversity of groundwater bodies (GWBs) in large French administrative regions pose challenges to their monitoring and protection by regional health agencies. To overcome this obstacle, we propose, for the Auvergne-Rhône-Alpes region (about 70,000 km2), a grouping of GWBs into homogeneous groups based on the sources of variability within a large dataset of groundwater physico-chemical and bacteriological characteristics (8078 observations and 13 parameters). This grouping involved a dimensional reduction in the data hyperspace by principal component analysis (PCA) and a clustering based on the mean values of each GWB on the factorial axes. The information lost when clustering from the sample point scale to the GWB scale and then to that of the GWB group was quantified by analysis of variance and showed that grouping GWBs is accompanied by a small loss of information. A discriminant analysis confirmed the high spatial and temporal variability within the dataset, as well as the effectiveness of the proposed method for establishing homogeneous sets. Some roadmaps for more targeted monitoring of water resources were briefly proposed.

Publisher

MDPI AG

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