Optimal Trade-Off of Dynamic Water Resources Spatio-Temporal Allocation Considering Seasonal Variations: A Multi-Stage Multi-Objective Programming with Hybrid Uncertainty and Risk Preference

Global population proliferation and climate variability have exacerbated water scarcity crises worldwide. Conventional static allocation methodologies inadequately address inherent seasonal fluctuations, multi-objective conflicts, and hybrid uncertainties in river basin water resources allocation systems. This study establishes a dynamic multi-stage water resources allocation optimization framework that integrates economic efficiency, environmental protection, and social equity while incorporating hybrid uncertainty and decision-makers’ risk preferences to characterize systemic risks in water resources allocation. The framework employs a four-stage seasonal decomposition with state transition mechanisms, establishing a multi-stage multi-objective optimization model for economic benefit maximization, pollutant emission (COD and ammonia nitrogen) minimization, and water shortage rate minimization. Fuzzy stochastic variables are introduced to address water quantity uncertainty, while the Value-at-Risk (VaR) methodology is employed to characterize managers’ risk preferences. Additionally, an improved U-NSGA-III algorithm is utilized to solve the complex multi-objective optimization problem. The Minjiang River Basin, which encompasses eight municipalities and three water-consuming sectors (agriculture, industry, and domestic) serves as a validation case study. Results demonstrate that the dynamic allocation framework achieves optimal seasonal economic benefits ranging from 4.19 × 1011 to 5.47 × 1012 CNY through efficacious inter-temporal water transfer mechanisms while maintaining zero water shortage in all periods. Algorithm convergence precision is improved by 35%, and solution-set quality is enhanced by 28%. Multi-objective trade-off analysis reveals distinct seasonal conflict patterns, with economic-environmental correlation coefficients varying from 0.30 in spring to 0.63 in autumn, indicating intensified “high-efficiency-high-pollution” conflicts during production peak periods. Spatial allocation exhibits pronounced regional functional differentiation. This research provides a novel theoretical framework and practical optimization tool for sustainable basin water resources management, offering crucial insights for addressing complex multi-objective trade-offs, seasonal dynamics, and uncertainty management in integrated water resources planning under climate change conditions.