This work presents a novel time-lapse modelling scheme for Airborne Electromagnetics (AEM) monitoring datasets, applied to study the hydro-related evolution of the Bookpurnong floodplain in South Australia. Additionally, it introduces a new wide-ranging approach for this type of study, incorporating new processing, validation, and interpretation tools. Time-Lapse studies are widespread in the literature but are not commonly applied to model EM data, particularly AEM data. This is likely due to the challenges of performing overlapping acquisitions with inductive systems. The key features of the new time-lapse scheme, which address these issues, include the definition of independent forward and model meshes. Additionally, a novel dedicated processing workflow for AEM monitoring is presented. The results of the time-lapse geophysical models are evaluated with an Independent Hydrogeological Validation (IHV), designed to support the geophysical results validation and interpretation phases with hydrogeological assessment of the system. At Bookpurnong, along a sector of the Murray River floodplain, multitemporal AEM survey were collected in 2015, 2022 and 2024, to study the groundwater system evolution over time. The time-lapse models show very small variations compared to the independent ones, while revealing sharply bounded variation zones over the floodplain. This demonstrates the effectiveness of the new time-lapse scheme, especially considering the discrepancies in data location and acquisition height among datasets. The AEM models are first validated through comparison with resistivity borehole measurements. The models are ultimately validated and interpreted using the IHV approach, which revealed a direct correlation between the hydrological stress of the Murray River and the response of the shallow aquifers. We believe that the time-lapse methodology developed in this work can be applied to AEM multitemporal studies for monitoring different processes, surpassing the results of single-time AEM investigations and providing a new dimension for studying large-scale processes with greater accuracy.