An uncertain agent-based model for socio-ecological simulation of groundwater use in irrigation: A case study of Lake Urmia Basin, Iran

Abstract

Socio-ecological systems include diverse resources of complexity involving complicated interactions, feedback, dynamic behavior patterns, heterogeneity in agents’ characteristics and behaviors, as well as system uncertainties. Agent-based modeling is introduced as a tool to simulate different levels of stakeholders and interactions, including individual, group, and institutional agents in complex socio-ecological systems. In the present study, an agent-based model was developed to explore sustainable solutions in uncertain conditions for groundwater restoration of a critical aquifer in the Lake Urmia Basin, Iran. Different projects for aquifer restoration such as wells monitoring, license adjustment, and modern irrigation development were implemented in the developed framework. The results showed that it is possible to alleviate the negative balance of the aquifer in case of necessary coordination in the projects implementation. So that during the simulation period, while increasing the total annual net income of the cultivation by €1.3 million (about 7%), the groundwater level drawdown can be compensated by about 5 m (about 50% of the decline over the past 30 years). Results of sensitivity analysis on the risk-taking behavior of farmer agents showed that it has a more significant impact on the total income than groundwater level. In the risk-seeking society conditions, a 10% increase of total annual net income is expected in the early years of simulation. The framework is used to learn about the influences of agents’ objectives, characteristics, and behaviors on the system variability. Systematic analysis in such a framework provides a better understanding of system structures to support decision-making under uncertainty in a participatory process.