A Hybrid System Dynamics–Agent-Based Modelling Framework for Adaptive Governance of Decentralized Water Circularity

Water scarcity is intensifying globally, particularly in regions facing chronic stress, underscoring the need to shift from linear water management toward circular, adaptive strategies. This study develops a tightly coupled Hybrid SD–ABM (Hybrid System Dynamics Agent-Based Modelling) framework, implemented in Stella Architect, to evaluate decentralized wastewater reuse as a pathway to enhance water circularity and resource recovery. The framework integrates macro‑level feedback loops with micro‑level agent behaviors, linking plant‑level treatment performance, dynamic effluent‑quality–behavior feedback loops, and policy incentives within a single multi‑scale simulation environment. Results show that reuse adoption increases substantially when treated wastewater is priced below €0.15/m³ and farm subsidies exceed 30% of irrigation costs, while effluent quality emerges as a critical determinant of user trust. The model also demonstrates environmental and operational gains from precision irrigation and nutrient recovery, including biogas offsetting. An embedded optimization analysis reveals a trade‑off between maximizing reuse and maintaining financial sustainability for the water authority. By capturing the advancement of infrastructure performance, user behavior, and regulatory interventions, this work advances Hybrid SD–ABM modelling and provides a decision‑support tool for adaptive water governance in water‑scarce contexts.