Exploring multiscale variability in groundwater quality: a comparative analysis of spatial and temporal patterns via clustering

• Autor(a) principal: Mohsine, Ismail
• Data de Publicação: 2023
• Outros Autores: Kacimi, Ilias, Abraham, Shiny, Valles, Vincent, Barbiero, Laurent, Dassonville, Fabrice, Bahaj, Tarik, Kassou, Nadia, Touiouine, Abdessamad, Jabrane, Meryem, Touzani, Meryem, El Mahrad, Badr, Bouramtane, Tarik
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.1/19603
• topic: Groundwater quality; European Union Water Framework Directive; Groundwater Bodies; Hydrogeological clusters; Environmental outliers; PACA region of France

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-Cote 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-2 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-2 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.