| Summary: | The modeling of hydrogeological processes often involves a quantitative description of complex systems in which a limited dataset is available, bringing about the formulation of conceptual models able to describe them in a simplified framework. In order to evaluate the reliability of these conceptual models, a statistical description of the elements composing the system can be useful, especially with reference to their mutual interactions. This study shows, through some applicative examples in the hydrogeological field, that the statistical analysis of characterizing the parameters and cause–effect relations arising from time series monitoring data can give useful information about the system dynamic, thus contributing to updating the conceptual model and therefore improving the results of following numerical modeling. Indeed, this dynamic description of the system, with the introduction of the verification and validation processes of the conceptual model, allows the correction of possible errors due to a lack of data or the phenomenon’s complexity. This leads to many hydrogeological issues, such as the identification of the most productive aquifer or the one that has the highest vulnerability to pollution, as well as zones of interest in groundwater flow that can trigger slope instability.
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