New water resources management methodologies are needed to address increasing demands and future uncertainty for urban water resources. Adaptive water demand management strategies provide an approach to improve the efficiency of water system operations and meet water demands by adapting flexibility to increasing stresses, such as droughts. This study simulates adaptive water demand management through the development of a complex adaptive system modeling framework, which couples cellular automata modeling, agentbased modeling, and hydrologic modeling to simulate land-use change, consumer behaviors, management decisions, the rainfall-runoff process, and reservoir storage. The model is applied to simulate the effect of demand management strategies on reductions in municipal water demands and on the sustained storage in a surface water supply reservoir. Historic and projected climate change hydroclimatic time series are used to assess the effectiveness of domestic water restrictions, including outdoor watering restrictions, a rainwater harvesting rebate program, and a high-density land-use change policy. Strategies are adaptively implemented based on the amount of water storage available. The framework is applied to evaluate strategies for the Arlington, Texas, metropolitan area that historically suffers from severe droughts. The framework provides an approach to evaluate a combination of multiple strategies for effectively managing the increasing stresses caused by urbanization, population growth, and climate change. Results demonstrate that adaptive demand management strategies that respond to water shortages result in long-term per capita demand reductions. For climate projections that forecast severe water shortages, development density strategies are more effective than rainwater harvesting strategies, and a combination of strategies can reduce the need for interbasin transfers and maintain reservoir volumes. (C) 2015 American Society of Civil Engineers.