Spatiotemporal characterisation of Milan’s groundwater system through statistical techniques

Milan city faces challenges in urban water management due to fluctuating groundwater levels. To better characterize the groundwater system, this study aimed at identifying spatiotemporal patterns in piezometric data and exploring their relationship with different factors through statistical techniques. Groundwater level data were divided into historical and recent periods and analysed for autocorrelation and correlation with gridded precipitation data, pumped volumes, and imperviousness data at different temporal scales. Results revealed an overall rising trend of Milan’s water table with groundwater levels clustering in six groups at different areas from north to south of Milan, suggesting the influence of site-specific characteristics. Insights into the interplay of drivers influencing Milan’s groundwater system reveal a weak correlation with precipitation, alongside an inverse correlation with groundwater withdrawals and land use change. This study evidences the complexity of this system, offering a basis for future research to support sustainable urban water management.