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<p>This paper introduces a novel water allocation framework that integrates an adaptive decision-making behavioral model with an agent-based model (ABM), enhanced by drought indicators. By embedding numerical weather predictions (NWPs) into the model, the framework dynamically anticipates drought conditions, allowing agricultural agents to adjust their decisions based on projected environmental stressors. This addresses a critical challenge in arid regions, where farmers’ perceptions of drought severity heavily influence water-conserving practices and crop choices. Advancing traditional ABMs by incorporating real-time drought indicators and behavioral adaptation mechanisms, the model enables a more realistic simulation of farmers’ decision-making under different weather conditions. Through scenario-based analysis, policy interventions are evaluated for their effects on farmers’ livelihoods and regional hydrology. The Borda count method ranks scenarios based on aquifer stability, economic well-being, and cost-effectiveness of water-saving measures to identify the optimal strategy. Applied to the water-stressed Ardabil Plain in Iran, results show 63.34% of farmers proactively adopt conservation measures under drought anticipation. The top scenario increases low-water-demand crop cultivation by 8.36% and reduces groundwater withdrawals by 7.78% over the planning horizon. These findings illustrate the framework’s potential as a data-driven tool for sustainable agricultural planning, balancing ecological resilience with socio-economic stability in drought-prone regions.</p>

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An Adaptive Agent-Based Framework for Simulating Farmers’ Behavior in the Face of Drought: Application of Numerical Weather Predictions

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Negin Salimi,
  • Mohammad Ferdosian,
  • Najmeh Mahjouri

摘要

This paper introduces a novel water allocation framework that integrates an adaptive decision-making behavioral model with an agent-based model (ABM), enhanced by drought indicators. By embedding numerical weather predictions (NWPs) into the model, the framework dynamically anticipates drought conditions, allowing agricultural agents to adjust their decisions based on projected environmental stressors. This addresses a critical challenge in arid regions, where farmers’ perceptions of drought severity heavily influence water-conserving practices and crop choices. Advancing traditional ABMs by incorporating real-time drought indicators and behavioral adaptation mechanisms, the model enables a more realistic simulation of farmers’ decision-making under different weather conditions. Through scenario-based analysis, policy interventions are evaluated for their effects on farmers’ livelihoods and regional hydrology. The Borda count method ranks scenarios based on aquifer stability, economic well-being, and cost-effectiveness of water-saving measures to identify the optimal strategy. Applied to the water-stressed Ardabil Plain in Iran, results show 63.34% of farmers proactively adopt conservation measures under drought anticipation. The top scenario increases low-water-demand crop cultivation by 8.36% and reduces groundwater withdrawals by 7.78% over the planning horizon. These findings illustrate the framework’s potential as a data-driven tool for sustainable agricultural planning, balancing ecological resilience with socio-economic stability in drought-prone regions.