@article{fdi:010092777,
  title = {{D}iscrimination of spatial and temporal variabilities in the analysis of groundwater databases : application to the {B}ourgogne-{F}ranche-{C}omt{\'e} {R}egion, {F}rance},
  author = {{B}ousouis, {A}. and {B}ouabdli, {A}. and {A}yach, {M}. and {L}azar, {H}. and {R}avung, {L}. and {V}alles, {V}. and {B}arbi{\'e}ro, {L}aurent},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his 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. {G}roundwater quality data from the {B}ourgogne-{F}ranche-{C}omt & eacute; region of {F}rance forms the basis of this investigation. {S}pecifically, the {SISE}-{EAUX} database includes 3569 groundwater samples collected over various dates from 989 monitoring points. {T}he 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. {T}hese datasets enable a separate analysis of spatial and temporal variances. {P}rincipal component analysis ({PCA}) and parameter hierarchical clustering were used to compare the results, yielding valuable insights into the underlying processes. {T}his 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. {S}uch differentiation enhances the understanding of fecal contamination vectors and nitrate pollution, which are often linked to surface and subsurface runoff in vulnerable catchment areas. {W}hile conceptually clear, the practical separation of spatial and temporal variability presents challenges. {F}or example, catchments sensitive to surface water inflows during rainfall events are unevenly distributed across the region, correlating with specific natural environments. {A}s a result, areas of high temporal variability are also well-structured spatially, underscoring the interdependence of these two types of variability. {T}his complexity is exemplified by the behavior of iron, which varies in association with surface and subsurface parameters depending on spatial and temporal contexts. {A}dditionally, asynchronous sampling and varying frequencies across sites lead to discrepancies in the average temporal data acquisition between points. {T}hese disparities can influence spatial variability calculations, as temporal variability effects are not entirely removed. {D}espite these challenges, the distinction between spatial and temporal components is essential for a deeper understanding of groundwater quality mechanisms. {T}his refined approach improves diagnostic precision and supports more targeted and effective water resource management strategies.},
  keywords = {groundwater database ; bacteriological composition ; chemical composition ; principal component analysis ; clustering ; {FRANCE}},
  booktitle = {},
  journal = {{W}ater},
  volume = {17},
  numero = {3},
  pages = {384 [17 p.]},
  year = {2025},
  DOI = {10.3390/w17030384},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092777},
}