Agent-based modeling for demand management of reservoirs considering social and hydrological interactions under uncertainty.

Abstract

Water demand management is a topical research activity; this study uses agent-based modeling to simulate the impact of water demand management on a reservoir, integrating social and hydrological environments. The social environment incorporates questionnaires to determine participation rates in four demand reduction scenarios, which comprise (i) drought awareness, (ii) price increases, (iii) education/advertising, and (iv) provision of water-saving facilities. The hydrological environment utilizes the balance equation to calculate the water volume in a reservoir. Agents include the reservoir manager and urban, agricultural, and industrial demands. Monte Carlo simulation also conducted an uncertainty analysis to capture the uncertainty in social and hydrological environments. Maragheh, a city in northwestern Iran with a 60 MCM reservoir, was selected as a case study. The results demonstrate that despite population growth, all scenarios maintain higher reservoir volumes than a baseline scenario without demand management. These scenarios meet water demand and produce a 0.4 to 0.7 MCM annual surplus. Providing water-saving facilities maximizes annual reservoir volume and surplus flow and prevents reservoir depletion even under worst-case uncertainty. Annual volume increases from 16 MCM without demand management to 36 MCM under the most effective scenario.