Largely because water resource planning in the U.S. has been separated from land-use planning, opportunities for explicitly linking planning policies to water availability remain unexamined. The pressing need for better coordination between land-use planning and water management is amplified by changes in the global climate, which will place even greater importance on managing water supplies and demands than in the past. By surveying land and water managers in two urbanizing regions of the western United States—Portland, Oregon and Phoenix Arizona—we assessed the extent to which their perspectives regarding municipal water resource management align or differ. We specifically focus on characterizing how they perceive water scarcity problems (i.e., stressors) and solutions (i.e., strategies). Overall, the results show a general agreement across both regions and professions that long-term drought, population growth, and outdoor water use are the most important stressors to urban water systems. The results of the survey indicated more agreement across cities than across professions with regard to effective strategies, reinforcing the idea that land-use planners and water managers remain divided in their conception of the solutions to urban water management. To conclude, we recommend potential pathways for coordinating the fields of land and water management for urban sustainability.

Recent evidence suggests that urban forms and materials can help to mediate temporal variation of microclimates and that landscape modifications can potentially reduce temperatures and increase accessibility to outdoor environments. To understand the relationship between urban form and temperature moderation, we examined the spatial and temporal variation of air temperature throughout one desert city -Doha, Qatar - by conducting vehicle traverses using highly resolved temperature and GPS data logs to determine spatial differences in summertime air temperatures. To help explain near-surface air temperatures using land cover variables, we employed three statistical approaches: Ordinary Least Squares (OLS), Regression Tree Analysis (RTA), and Random Forest (RF). We validated the predictions of the statistical models by computing the Root Mean Square Error (RMSE) and discovered that temporal variations in urban heat are mediated by different factors throughout the day. The average RMSE for OLS, RTA and RF is 1.25, 0.96, and 0.65 (in Celsius), respectively, suggesting that the RF is the best model for predicting near-surface air temperatures at this study site. We conclude by recommending the features of the landscape that have the greatest potential for reducing extreme heat in arid climates.