Andrea Manzoni

In Italy, a significant portion of the population relies on underground sources for their drinking water. However, these sources face the constant risk of contamination from various sources, both known and newly emerging. This vulnerability arises from changes in the flow of groundwater and recharge patterns, which are influenced by shifting climatic conditions. Unfortunately, existing studies on this topic in the Italian context have been limited to modeling individual aquifers under stable conditions. To effectively address the impacts of climate change on these water systems, it is crucial to adopt a comprehensive approach that considers the interactions and interdependencies among various components within the region. While modeling at this broader scale should not replace the detailed examination of individual aquifers, it should be viewed as a complementary tool. By adopting a synergistic modeling approach, we can identify critical areas and establish boundary conditions necessary for the development of multiscale models. Overall, it is imperative to expand our understanding of the issue holistically, taking into account the broader context and dynamics, in order to effectively tackle the challenges posed by climate change and ensure the sustainable management of drinking water resources in Italy.

Our research aims to investigate novel approaches for comprehending and forecasting the behavior of contaminants within the groundwater environment. Specifically, we seek to achieve this understanding under both present-day conditions and the potential changes brought about by climate change, all on a regional scale.

Conceptual and numerical modeling combining physics-based and data-driven machine learning models.

Our research yields significant findings in the modeling of large-scale flow and contaminant transport dynamics. By employing advanced modeling techniques, we are able to analyze and understand the complex behaviors exhibited by these processes on a larger scale.