The Challenge of Predicting Future Urban Water Demand: A System Dynamics Modeling Approach

Previous research investigating urban residential water demand has either employed social science methods to examine human attitudes and behaviors or statistical models to explain the impacts of climate and other biophysical processes. The lack of integration of social science and natural science methods to analyze patterns of urban water demand has resulted in limited understanding of the coupled natural and human systems. Furthermore, while statistical models have the ability to investigate multiple determinants, both social and ecological, they do not model the effects of complex feedback loops among variables within an ever changing environment. System dynamics modeling improves upon traditional statistical models by more accurately representing the complexity and dynamism inherent in coupled human and natural systems. This research examines residential water demand in Hillsboro, Oregon, a rapidly growing municipality in the Portland metropolitan area, using a system dynamics modeling approach to examine the multiple determinants, stresses, and interactions that influence urban water demand. By incorporating dynamic ecological, demographic, behavioral, and land use variables, the model accounts for the complex relationships among each of the variables and elucidates potential tipping points, beyond which abrupt and surprising changes may occur. The model is run multiple times simulating changes to the system for 55 years (1995 – 2050) under various climate change, population growth, land use change and conservation scenarios. The findings of this study have significant policy implications and reveal the potential for system dynamics modeling to be integrated in decision-support tools in a changing environment.

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