This study applies a two-way coupled agent-based model (ABM) with a river-reservoir management model (RiverWare) to analyze the role of risk perception in water management decisions using the Bayesian inference mapping joined with the cost–loss model. The calibration results capture the dynamics of historical irrigated area and streamflow changes and suggest that the proposed framework improves the representation of human decision-making processes compared to conventional rule-based ABMs.