We investigate the ability of combining the Karhunen-Loève transform (KLT) with the kriging method to extract regional information from a set of point measurements. This method was applied to a set of 195 piezometric head time series over a period of 17 years from observation wells distributed within the French and German area of the Rhine valley alluvial groundwater body. Piezometric head time series are analyzed with KLT in order to highlight characteristic temporal signals, classified from the most energetic (global) to the least energetic (local) signals. The first five signals amount to 80% of the global variance of the system and are inferred to represent different hydrological contributions (exchanges with rivers and rainfall), but they also represent a significant anthropogenic component. Kriging is then used to regionalize the signals and to build a reconstruction model of the behavior of the whole aquifer containing only filtered information coming from identified source signals.