<p>Urban water supply systems are increasingly challenged by water scarcity, rising energy consumption, carbon emissions, and water pollution under rapid urbanization and climate change. Existing studies have mainly focused on optimizing individual objectives such as water allocation or energy efficiency, while limited attention has been paid to the coordinated optimization of the Water-Energy-Carbon-Pollution (WECP) nexus under uncertainty. This study develops an integrated multi-objective optimization framework for the WECP nexus using Zhengzhou, China, as a representative case. First, a coupling coordination degree model is established to evaluate the interactions among water conservation, energy saving, carbon reduction, and pollution control. Subsequently, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), integrated with interval parameters and dynamic constraints, is employed to optimize multi-source water allocation while balancing economic, social, and environmental objectives. Furthermore, an elastic storage mechanism is introduced to improve system adaptability under uncertain water demand and climate variability.The results indicated that the optimized allocation significantly enhanced system coordination, with the coupling coordination degree increasing from 0.67 to 0.87. Groundwater extraction decreased by 18.5%, reclaimed water utilization increased to 14.7%, and annual carbon emissions were reduced by approximately 57,000 t CO2, while maintaining economic benefits and reducing water shortages. Compared with conventional optimization methods, the proposed framework provided a more balanced trade-off among competing objectives and improved decision-making flexibility through Pareto-optimal solutions. This study demonstrated the potential of integrated WECP optimization for promoting sustainable urban water management and provided practical guidance for low-carbon transformation of water supply systems in Zhengzhou and other water-scarce cities.</p>