Exploring Multiscale Variability in Groundwater Quality: A Comparative Analysis of Spatial and Temporal Patterns via Clustering
Defining homogeneous units to optimize the monitoring and management of groundwater is a key challenge for organizations responsible for the protection of water for human consumption. However, the number of groundwater bodies (GWBs) is too large for targeted monitoring and recommendations. This stud...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/8/1603 |
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| author | Ismail Mohsine Ilias Kacimi Shiny Abraham Vincent Valles Laurent Barbiero Fabrice Dassonville Tarik Bahaj Nadia Kassou Abdessamad Touiouine Meryem Jabrane Meryem Touzani Badr El Mahrad Tarik Bouramtane |
| author_facet | Ismail Mohsine Ilias Kacimi Shiny Abraham Vincent Valles Laurent Barbiero Fabrice Dassonville Tarik Bahaj Nadia Kassou Abdessamad Touiouine Meryem Jabrane Meryem Touzani Badr El Mahrad Tarik Bouramtane |
| author_sort | Ismail Mohsine |
| collection | DOAJ |
| description | Defining homogeneous units to optimize the monitoring and management of groundwater is a key challenge for organizations responsible for the protection of water for human consumption. However, the number of groundwater bodies (GWBs) is too large for targeted monitoring and recommendations. This study, carried out in the Provence-Alpes-Côte d’Azur region of France, is based on the intersection of two databases, one grouping together the physicochemical and bacteriological analyses of water and the other delimiting the boundaries of groundwater bodies. The extracted dataset contains 8627 measurements from 1143 observation points distributed over 63 GWB. Data conditioning through logarithmic transformation, dimensional reduction through principal component analysis, and hierarchical classification allows the grouping of GWBs into 11 homogeneous clusters. The fractions of unexplained variance (FUV) and ANOVA R<sup>2</sup> were calculated to assess the performance of the method at each scale. For example, for the total dissolved load (TDS) parameter, the temporal variance was quantified at 0.36 and the clustering causes a loss of information with an R<sup>2</sup> going from 0.63 to 0.4 from the scale of the sampling point to that of the GWB cluster. The results show that the logarithmic transformation reduces the effect of outliers and improves the quality of the GWB clustering. The groups of GWBs are homogeneous and clearly distinguishable from each other. The results can be used to define specific management and protection strategies for each group. The study also highlights the need to take into account the temporal variability of groundwater quality when implementing monitoring and management programs. |
| first_indexed | 2024-03-11T04:25:58Z |
| format | Article |
| id | doaj.art-38428e9fccc049b79efe9d945501b9fa |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T04:25:58Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-38428e9fccc049b79efe9d945501b9fa2023-11-17T21:49:26ZengMDPI AGWater2073-44412023-04-01158160310.3390/w15081603Exploring Multiscale Variability in Groundwater Quality: A Comparative Analysis of Spatial and Temporal Patterns via ClusteringIsmail Mohsine0Ilias Kacimi1Shiny Abraham2Vincent Valles3Laurent Barbiero4Fabrice Dassonville5Tarik Bahaj6Nadia Kassou7Abdessamad Touiouine8Meryem Jabrane9Meryem Touzani10Badr El Mahrad11Tarik Bouramtane12Geosciences, Water and Environment Laboratory, Faculty of Sciences Rabat, Mohammed V University, Rabat 10000, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences Rabat, Mohammed V University, Rabat 10000, MoroccoElectrical and Computer Engineering Department, Seattle University, Seattle, WA 98122, USAMixed Research Unit EMMAH (Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes), Hydrogeology Laboratory, Avignon University, 84916 Avignon, FranceInstitut de Recherche pour le Développement, Géoscience Environnement Toulouse, CNRS, University of Toulouse, UMR 5563, 31400 Toulouse, FranceARS (Provence-Alpes-Côte d’Azur Regional Health Agency), 132, Boulevard de Paris, CEDEX 03, 13331 Marseille, FranceGeosciences, Water and Environment Laboratory, Faculty of Sciences Rabat, Mohammed V University, Rabat 10000, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences Rabat, Mohammed V University, Rabat 10000, MoroccoLaboratoire de Géosciences, Faculté des Sciences, Université Ibn Tofaïl, BP 133, Kénitra 14000, MoroccoLaboratoire de Géosciences, Faculté des Sciences, Université Ibn Tofaïl, BP 133, Kénitra 14000, MoroccoNational Institute of Agronomic Research, Rabat 10060, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences Rabat, Mohammed V University, Rabat 10000, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences Rabat, Mohammed V University, Rabat 10000, MoroccoDefining homogeneous units to optimize the monitoring and management of groundwater is a key challenge for organizations responsible for the protection of water for human consumption. However, the number of groundwater bodies (GWBs) is too large for targeted monitoring and recommendations. This study, carried out in the Provence-Alpes-Côte d’Azur region of France, is based on the intersection of two databases, one grouping together the physicochemical and bacteriological analyses of water and the other delimiting the boundaries of groundwater bodies. The extracted dataset contains 8627 measurements from 1143 observation points distributed over 63 GWB. Data conditioning through logarithmic transformation, dimensional reduction through principal component analysis, and hierarchical classification allows the grouping of GWBs into 11 homogeneous clusters. The fractions of unexplained variance (FUV) and ANOVA R<sup>2</sup> were calculated to assess the performance of the method at each scale. For example, for the total dissolved load (TDS) parameter, the temporal variance was quantified at 0.36 and the clustering causes a loss of information with an R<sup>2</sup> going from 0.63 to 0.4 from the scale of the sampling point to that of the GWB cluster. The results show that the logarithmic transformation reduces the effect of outliers and improves the quality of the GWB clustering. The groups of GWBs are homogeneous and clearly distinguishable from each other. The results can be used to define specific management and protection strategies for each group. The study also highlights the need to take into account the temporal variability of groundwater quality when implementing monitoring and management programs.https://www.mdpi.com/2073-4441/15/8/1603groundwater qualityEuropean Union Water Framework Directivegroundwater Bodieshydrogeological clustersenvironmental outliersPACA region of France |
| spellingShingle | Ismail Mohsine Ilias Kacimi Shiny Abraham Vincent Valles Laurent Barbiero Fabrice Dassonville Tarik Bahaj Nadia Kassou Abdessamad Touiouine Meryem Jabrane Meryem Touzani Badr El Mahrad Tarik Bouramtane Exploring Multiscale Variability in Groundwater Quality: A Comparative Analysis of Spatial and Temporal Patterns via Clustering Water groundwater quality European Union Water Framework Directive groundwater Bodies hydrogeological clusters environmental outliers PACA region of France |
| title | Exploring Multiscale Variability in Groundwater Quality: A Comparative Analysis of Spatial and Temporal Patterns via Clustering |
| title_full | Exploring Multiscale Variability in Groundwater Quality: A Comparative Analysis of Spatial and Temporal Patterns via Clustering |
| title_fullStr | Exploring Multiscale Variability in Groundwater Quality: A Comparative Analysis of Spatial and Temporal Patterns via Clustering |
| title_full_unstemmed | Exploring Multiscale Variability in Groundwater Quality: A Comparative Analysis of Spatial and Temporal Patterns via Clustering |
| title_short | Exploring Multiscale Variability in Groundwater Quality: A Comparative Analysis of Spatial and Temporal Patterns via Clustering |
| title_sort | exploring multiscale variability in groundwater quality a comparative analysis of spatial and temporal patterns via clustering |
| topic | groundwater quality European Union Water Framework Directive groundwater Bodies hydrogeological clusters environmental outliers PACA region of France |
| url | https://www.mdpi.com/2073-4441/15/8/1603 |
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