UCLA

This research contributes to a better understanding of the dynamics of single-family water consumption associated with vegetation in semi-arid cities. The innovative research approach couples long-term water consumption data with remote-sensing based products, socio-demographic, land cover, landscaping and climate data analyzed with multidisciplinary techniques. Accurate water demand forecasting and long-term conservation planning are required to meet future urban water needs relying on a set of integrated sustainable resources. In the face of climate change and urbanization growth, quantifying and predicting ecosystems costs related to irrigation and benefits are important and challenging. To address these needs, the first part of this study focuses on analyzing trends and determinants in single-family water use in Los Angeles indicating that the current water rate structure can be optimized to achieve higher water savings: Tier 2 water rates do not lead to more conservation behaviors, though they were implemented to do so. It also shows that residential landscape is primarily maintained by irrigation and is not correlated with the seasonal precipitation pattern. The analysis of the effectiveness of water restrictions during the last drought in Los Angeles confirms that there is still room for outdoor water conservation: there was no significant change in the landscape greenness during the implementation of the watering restrictions. Conservation programs focusing on efficient watering practices can lead to higher water savings while supporting healthy landscapes. Increased mandatory restrictions were more effective than voluntary restrictions during the drought period reaching a maximum of 23% water reduction in July-August 2009 in City-average single-family household water use. This suggests that outdoor water use savings may result from long-term conservation programs. This leads to the last part of this research work focusing on quantifying and predicting landscaping irrigation through the development of predictive models. The predictive regression model explores the relationship between single-family water use and vegetation greenness surplus estimated through remote-sensing vegetation indices that can be used as a predictive tool to target outdoor conservation measures. This project was integrated within a coupled socio-ecohydrological approach to address inter-disciplinary issues related to the urban landscape. It encourages the use of advanced models and remote-sensing products incorporated into policies that improve outdoor use modeling and predictions to guide future water demand management strategies under uncertain climate variability.