A disaggregated system dynamics and agent-based modeling of the water-energy-food nexus for optimizing water allocation

Asymmetric water distribution in agriculture is a significant threat to water, energy, and food security in water-limited areas. This work presents an integrated model framework based on System Dynamics and Agent-Based (SD-AB) modeling applying multi-objective evolutionary optimization with the non-dominated sorted genetic algorithm (NSGA)-II, and post-optimization analysis through Analytic Hierarchy Process (AHP) that facilitates sustainable water supply. The SD-AB model simulates the intricate interactions among energy consumption, water supply, agricultural yield, and economic outcomes. The NSGA-II resolves the trade-offs between energy, groundwater utilization, and agriculture net benefit, wherein water allocation to every crop represents decision variables and the groundwater level is treated as a state variable. The solutions are ranked by implementing the AHP technique considering environmental sustainability, water availability, and cost-effectiveness. Local hydrology, agriculture, and socio-economic data are used as model inputs. This work's framework was applied to the Zayandehrud Basin, Iran. The model outputs show that optimal allocation schemes cut water use by 13.1%, increase the average groundwater level by 1 m, decrease energy demand by 23.96%, and enhance environmental water allocation by 355% compared to the business-as-usual (BAU) baseline scenario, which is the current water allocation without optimization. Net agricultural benefit decreases by 22.4%, revealing a trade-off between returns and sustainability. This paper's methodology provides strong policy implications while it is constrained by uncertainty in data and behavioral assumptions. Overall, this paper's methodology represents a useful decision-support tool for sustainable water resource planning in water-scarce basins with possible extension to other socio-environmental situations worldwide.