Analysis and Predictive Models of Single-Family Customer Response to Water Curtailments During Drought

Authors

  • Austin S. Polebitski,

    1. Respectively, Research Assistant Professor (Polebitski) and Department Head and Professor (Palmer), Department of Civil and Environmental Engineering, University of Massachusetts – Amherst, 224 Marston Hall, 130 Natural Resources Road, Amherst, Massachusetts 01003-9293
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  • Richard N. Palmer

    1. Respectively, Research Assistant Professor (Polebitski) and Department Head and Professor (Palmer), Department of Civil and Environmental Engineering, University of Massachusetts – Amherst, 224 Marston Hall, 130 Natural Resources Road, Amherst, Massachusetts 01003-9293
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  • Paper No. JAWRA-11-0103-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until six months from print publication.

(E-Mail/Polebitski: polebitski@ecs.umass.edu).

Abstract

Polebitski, Austin S. and Richard N. Palmer, 2012. Analysis and Predictive Models of Single-Family Customer Response to Water Curtailments During Drought. Journal of the American Water Resources Association (JAWRA) 1-12. DOI: 10.1111/j.1752-1688.2012.00691.x

Abstract:  This research investigates customer response to demand management strategies during two drought periods in the City of Seattle. An analysis of customer response to voluntary water curtailments is conducted using k-means clustering to identify like groups of customers and behavior patterns. The clustering method identified important variables (household income, lot size, living space, and family size) useful in determining customer response to water curtailments. Ordinary least squares and spatial lag regression models are estimated using the first and second principal components of variables identified in the clustering analysis. Larger values of income, lot size, and living space enhanced water reductions whereas larger family size tended to reduce the effectiveness of curtailments. Projections of changes in Seattle’s built environment and demographics between 2000 and 2030 were obtained from an urban simulation model (UrbanSim) and were processed through the regression models to investigate changes in future curtailment effectiveness. This research found that increasing household size hardened demands (decreased curtailment effectiveness) whereas decreasing household size increased per-capita curtailment effectiveness. These results suggest that changes in the number of residents within a home is likely to be the most important factor in determining future curtailment effectiveness.

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