Application and comparison of different statistical methods for the analysis of groundwater levels over time: Response to rainfall and resource evolution in the Piedmont Plain (NW Italy)

Abstract

Monitoring and analysis of groundwater level (GWL) in space and time is one of the tools used to evaluate the quantitative status of groundwater (GW) resources and identify possible alterations and critical cases due to climate change and variability, anthropogenic influences and other driving factors.In this study, four statistical methodologies (trend, change-point, percentile and non-standardized anomaly analyses) were applied for GWL and rainfall (R) analysis in the Piedmont Plain (western Po Plain, NW Italy). To detect the interannual variations in the GW maximum annual amplitude, the coefficient of variation was also used.The aims of the study were 1) to compare the results of different statistical methods, highlighting their applicability and differences to evaluate the quantitative evolution of GW, 2) to identify the relationship between GWL and R, 3) to investigate the spatiotemporal variation in the GWL of shallow aquifers in the Piedmont Plain, and 4) to describe critical situations of GW depletion.The study highlights that the application of a single method for assessing the shallow GW resource status does not always guarantee a reliable evaluation. For this reason, it is advisable to apply different analysis methods at the same time. Completeness of data and medium to long time series are prerequisites for meaningful analyses. The use of the same time interval is always necessary for comparisons between different monitoring wells and between the results of different statistical analyses. Last, by spatializing the results, it was possible to identify areas characterized by similar GWL behaviour due to hydrological structure, climate variability, land use and the evolution of anthropogenic activities over time. These factors influence vary locally in the Piedmont plain and require local assessments to determine the impact of changes in GWL.