Zarei et al. (2025) A disaggregated system dynamics and agent-based modeling of the water-energy-food nexus for optimizing water allocation

Identification

• Journal: Scientific Reports
• Year: 2025
• Date: 2025-10-07
• Authors: Soheila Zarei, Omid Bozorg‐Haddad, Mohammad Reza Nikoo, Hugo A. Loáiciga
• DOI: 10.1038/s41598-025-18838-6

Research Groups

• Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran.
• Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
• Department of Geography, University of California, Santa Barbara, CA, USA.

Short Summary

This study develops an integrated System Dynamics and Agent-Based (SD-AB) model with multi-objective optimization (NSGA-II) and post-optimization analysis (AHP) to optimize water allocation in the Zayandehrud Basin, Iran. The model demonstrates that optimal allocation significantly reduces water and energy consumption while increasing groundwater levels and environmental water allocation, albeit with a trade-off in net agricultural benefit.

Objective

• To develop and apply an integrated System Dynamics and Agent-Based (SD-AB) modeling framework, coupled with multi-objective evolutionary optimization (NSGA-II) and post-optimization analysis (AHP), to optimize water allocation and facilitate sustainable water supply within the water-energy-food (WEF) nexus in water-limited areas.
• To resolve trade-offs between energy consumption, groundwater utilization, and agriculture net benefit by optimizing water allocation to various crops, treating groundwater level as a state variable.

Study Configuration

• Spatial Scale: Zayandehrud Basin, Iran (~27,000 km²), disaggregated into 14 agricultural subregions (agents).
• Temporal Scale: 20-year simulation period (2001–2021) with an annual time step.

Methodology and Data

• Models used:
  • Hybrid System Dynamics (SD) and Agent-Based (AB) model (implemented in AnyLogic).
  • Multi-objective evolutionary optimization: Non-dominated Sorting Genetic Algorithm (NSGA-II).
  • Post-optimization analysis: Analytic Hierarchy Process (AHP).
  • FAO Penman–Monteith evapotranspiration models for crop water requirements.
• Data sources:
  • Historical hydrological records (2001–2021): Precipitation, evapotranspiration, infiltration, surface/groundwater flows and returns, groundwater levels, runoff, surface water (SW) and groundwater (GW) demand (Iran Water Resource Management Company - IWRMC).
  • Agricultural data: Crop-specific land use, yield, irrigation need, livestock water use, production cost, price (Agricultural Jihad Organization of Isfahan Province, Ministry of Agriculture - Jihad).
  • Energy data: Electricity use for irrigation (Ministry of Energy of Iran, Electricity Production and Distribution Company of Isfahan Province).
  • Socio-economic data: Per capita food demand, population (Statistical Center of Iran - SCI).

Main Results

• Optimal allocation schemes reduced total water use by 13.1% (from 4.398 x 10^9 m³ to 3.820 x 10^9 m³) compared to the business-as-usual (BAU) baseline.
• Average groundwater levels increased by 1 meter under the optimal solution.
• Energy demand decreased by 23.96% (from 392.83 gigajoules to 298.74 gigajoules) compared to the BAU baseline, leading to an approximate reduction of 15.7 metric tons (15,700 kg) of CO₂ emissions.
• Environmental water allocation increased by 355% (from approximately 14 x 10^6 m³ to 65 x 10^6 m³ annually) compared to the BAU baseline, supporting ecosystem health in the Gavkhuni wetland.
• Net agricultural benefit decreased by 22.4% (from 1,649 billion Rials to 1,243 billion Rials) under the optimal solution, revealing a trade-off between economic returns and sustainability objectives.
• The Food Security Index (FSI) improved by 21% on average across crops under the optimal solution, particularly for wheat and orchards.
• Sensitivity analysis showed that a 20% reduction in rice cultivation area led to a 1.8% reduction in irrigation water use (78 x 10^6 m³/year), a 19.3% drop in agricultural electricity consumption (1.53 gigajoules/year), and only a 0.6% decrease in net agricultural benefit.

Contributions

• Developed a novel hybrid SD–AB model that integrates systemic interconnections with agent-level behavior within the agricultural WEF nexus, addressing limitations of conventional models.
• Integrated the hybrid SD-AB model with NSGA-II for multi-objective optimization and AHP for post-optimization evaluation, providing a comprehensive decision-support system for WEF governance.
• Quantified the trade-offs and co-benefits across water, energy, food, and economic sectors, offering a robust tool for sustainable resource planning in water-scarce basins.
• Demonstrated the policy implications of optimal water allocation strategies, including reduced water and energy use, increased groundwater levels, enhanced environmental flows, and improved food security, while acknowledging economic trade-offs.

Funding

• Iran’s National Science Foundation (INSF)

Citation

@article{Zarei2025disaggregated,
  author = {Zarei, Soheila and Bozorg‐Haddad, Omid and Nikoo, Mohammad Reza and Loáiciga, Hugo A.},
  title = {A disaggregated system dynamics and agent-based modeling of the water-energy-food nexus for optimizing water allocation},
  journal = {Scientific Reports},
  year = {2025},
  doi = {10.1038/s41598-025-18838-6},
  url = {https://doi.org/10.1038/s41598-025-18838-6}
}