assessing the system-wide implications of a fully desalination-based supply strategy at the regional scale. As in the 2035 scenario, two analytical cases are considered, addressing climate-related uncertainty and social conditions affecting the acceptance and integration of desalinated water. This scenario enables the evaluation of long-term infrastructure pathways and their alignment with sustainability and water governance objectives for the basin. Across both the 2035 and 2045 scenarios, the expected benefits evaluated include improvements in water supply security for the mining operation, increased flexibility and robustness of the regional water network, and enhanced opportunities for coordinated water use among multiple stakeholders. Territorial impacts are analysed through the spatial representation of alternative infrastructure layouts, allowing comparison of shared and isolated configurations in terms of their interaction with land use and sensitive areas. Environmental impacts are assessed indirectly through the incorporation of environmental considerations into the cost rasters and multi-criteria evaluation framework, enabling a relative comparison of solutions based on their potential to reduce pressure on continental water resources and improve overall system sustainability. The results obtained from the application of the decision-support framework reveal a consistent set of outcomes across both analysed planning horizons, 2035 and 2045, providing a coherent picture of how regional water supply networks can be configured under different temporal assumptions. At the most general level, the analysis shows that, in both scenarios, the optimal network solutions integrate desalinated water sources, mining demand, existing infrastructure elements, and community water provision within a single regional supply structure. This indicates that shared water infrastructure emerges as a common organising principle of the system. At an intermediate level, the comparison between scenarios highlights differences in the role assigned to desalinated water within the regional network. In the 2035 scenario, the network configuration, shown in Figure 3, represents a transitional condition in which desalinated water is incorporated alongside existing water supply sources, enabling interactions with other basin users through exchange mechanisms (swaps). In contrast, the 2045 scenario (Figure 4) reflects a consolidated configuration in which desalinated water becomes the sole source of supply for the mining operation. While the regional network structure and the inclusion of community-related connections are preserved, the projected increase in demand associated with future operational expansion requires the consideration of additional desalination plants. Despite these differences in planning context, the overall composition of nodes and the spatial logic of the network remain comparable across both scenarios, providing a consistent basis for comparative analysis. At a more detailed level, in both scenarios, the transport routes generated by the tool traverse or pass near the localities included in the study, as a direct consequence of incorporating community supply nodes and exchange mechanisms into the network representation. This feature is present across all analysed cases, reinforcing the regional character of the proposed infrastructure configurations and distinguishing them from isolated, project-specific solutions. The analysis of sensitivity provides further insight into the stability of these results. Across both scenarios, variations in the input assumptions associated with climate-related conditions and with social factors do not modify the fundamental structure of the networks generated. The same supply sources, demand nodes, exchange connections, and general transport pipelines are consistently retained across all sensitivity cases. This indicates that the main network configurations are robust to the range of assumptions explored and are not driven by a narrow set of parameter values. At the most specific level, the sensitivity analysis reveals that variations in the input assumptions primarily affect the
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