Hydrologic processes such as rainfall, evaporation, runoff, and flood events drive the water cycle in water-scarce areas. Our ability to measure, control, and optimize these processes shapes the sustainability of water resources. In arid cities, urban lawns have been water guzzlers, leaving the federal agencies no choice but to remove high-water-use plant species. While this approach has saved thousands of gallons of water, its sustainability is questionable, as removing plants reduces evapotranspiration and increases heat. In addition, reducing pervious surfaces can cause flash flooding and heat-trapping in cities, discouraging outdoor activities. Water savings can be achieved sustainably by improving irrigation schedules, flood forecasting, and models to simulate vegetation-atmosphere interactions. However, the standardized way to devise an irrigation schedule is missing from the science of urban irrigation, and remote sensing satellites cannot monitor the local scale development and landscape diversity because of spatial scale issues. Therefore, this study explores a new way to collect high spatial resolution data using unmanned aerial vehicles and its applicability in urban hydrology. The application will be explored in three ways: 1. Improving irrigation water modeling, 2. Improving urban heat island models; and 3. Mapping urban floods more accurately. We used medium-sized drones with a payload as an L-band, albedometer, and TIR sensor along with a 360-degree camera flying at 5 m height that provides 60 cm spatial resolution data in the visible and thermal spectrum. We found that data from this platform can significantly improve urban irrigation and flood modeling improve. In addition, the dynamic values of surface albedo can improve atmospheric models such as the Weather Research and Forecasting (WRF) model by replacing the look-up table inputs with dynamic values, which are better representations of the actual surface. This study is helpfulfor urban climate scientists and water managers working to improve urban hydrological models.
