Discrimination of Spatial and Temporal Variabilities in the Analysis of Groundwater Databases: Application to the Bourgogne-Franche-Comté Region, France

Abstract

This study highlights the importance of distinguishing the mechanisms driving spatial and temporal variances in groundwater database analyses, with a particular focus on bacteriological contamination processes. Groundwater quality data from the Bourgogne-Franche-Comté region of France forms the basis of this investigation. Specifically, the SISE-EAUX database includes 3569 groundwater samples collected over various dates from 989 monitoring points. The methodology involves structuring the data into three distinct sets: (1) A spatio-temporal dataset without any conditioning; (2) A spatial dataset that assigns the mean values of each parameter to each sampling point; (3) A temporal dataset that captures deviations from the mean for each sampling point and parameter. These datasets enable a separate analysis of spatial and temporal variances. Principal component analysis (PCA) and parameter hierarchical clustering were used to compare the results, yielding valuable insights into the underlying processes. This analysis helps distinguish between factors related to geological or pedological spatial distributions and those associated with climatic events, such as intense rainfall episodes exhibiting seasonal patterns. Such differentiation enhances the understanding of fecal contamination vectors and nitrate pollution, which are often linked to surface and subsurface runoff in vulnerable catchment areas. While conceptually clear, the practical separation of spatial and temporal variability presents challenges. For example, catchments sensitive to surface water inflows during rainfall events are unevenly distributed across the region, correlating with specific natural environments. As a result, areas of high temporal variability are also well-structured spatially, underscoring the interdependence of these two types of variability. This complexity is exemplified by the behavior of iron, which varies in association with surface and subsurface parameters depending on spatial and temporal contexts. Additionally, asynchronous sampling and varying frequencies across sites lead to discrepancies in the average temporal data acquisition between points. These disparities can influence spatial variability calculations, as temporal variability effects are not entirely removed. Despite these challenges, the distinction between spatial and temporal components is essential for a deeper understanding of groundwater quality mechanisms. This refined approach improves diagnostic precision and supports more targeted and effective water resource management strategies.