This study examines the dynamics of user concerns in water supply systems under uncertain water availability and sectoral demand. We propose a hybrid adjustable robust optimization (ARO) model that integrates a multi-objective framework to address trade-offs between key performance indicators of the water supply system and its adaptive ability to respond to user concerns. The model is applied to the Hamoon Basin in southeastern Iran, a hyper-arid region experiencing chronic water scarcity. Real-time hydrological and demand data are used to calibrate uncertainty in rainfall and sectoral water demand across three scenarios: favorable, normal, and extreme. Subsequently, a series of comparative feasibility analyses is conducted at different levels of uncertainty to assess the frequency of user concerns in more detail. The final results indicate a significant decline in the performance of the water supply system under the extreme s3 scenario. For instance, the irrigation sector, identified as the largest water recipient, recorded the lowest frequency of failure (31% and 32%) and recovery delay indices (37% and 42%) in Zahedan and Zabul, respectively. These patterns indicate increased user concerns resulting from persistent unmet demand. The model highlights the establishment of strategic allocation rules aimed at prioritizing vulnerable sectors during water shortages, with particular emphasis on agriculture due to its economic sensitivity and substantial water demand. This proactive approach enhances the system's capacity to ensure equitable access and reduce disruptions during critical periods, especially in data-scarce or high-risk regions.