Resolving water security conflicts in agriculture by a cooperative Nash bargaining approach

Water scarcity is increasingly driven by socio-economic dynamics, climate change, and population growth. Conflicts among water users, including states, complicate sustainable water management, necessitating collaborative solutions. Building on important studies in water resource management, this study integrates the Water Evaluation and Planning (WEAP) model with the Nash bargaining solution to develop a fair water allocation system, which we apply to the Lower Bari Doab area of Punjab, Pakistan. This case study area is characterized by high demands for agricultural, domestic, industrial, and environmental water. To ensure fair allocation of water resources, we analyze future water trends, anticipate demands, and address supply-demand gaps under various scenarios. Our findings demonstrate that by utilizing WEAP in conjunction with the Nash bargaining solution, we can effectively achieve a balance between water demand and supply. This approach enables us to compare various water-use strategies and ensures fair distribution among agricultural stakeholders, who often have lower priority compared to the domestic, industrial, and environmental sectors. More importantly, we demonstrate that Nash bargaining solutions can be mutually beneficial and can maximize overall coverage of water demand. This approach ensures that all agents are better off compared to a non-cooperative outcome, promoting fairness and equity by balancing the needs of all parties. This integrated approach provides a robust framework for sustainable water management and is applicable to other regions facing similar challenges. • Management and allocation of agricultural water resources under increasing water scarcity is a challenging task. • Analysis of future water demands was conducted under various scenarios using WEAP. • We addressed the supply-demand gap among agricultural stakeholders by Nash bargaining solutions with homogeneous and heterogeneous weights. • Heterogeneous weights lead to more nuanced and socially optimal outcomes, addressing the diverse needs of all four agricultural agents.