Employing Thermal Imagery from Small Unmanned Aerial Systems to Model the Energy Balance of Urban Runoff

In urban areas, the combination of urban materials with low albedo, high volumetric heat capacity and heat-generating anthropogenic activities create urban heat islands of high air and land surface temperatures. This heat is exported during rainfall events in warm seasons to downstream water bodies, creating hydrologic urban heat islands. Models that simulate heat transfer of urban surfaces and stormwater runoff are necessary to developing solutions to this pressing issue; however, current models are constrained to micro or macro resolutions, neither of which represent the heterogeneity of spatially complex urban environments. Therefore, this research seeks to overcome these constraints by utilizing high-resolution remote sensing technologies at an urban subcatchment scale to calibrate and validate a spatially distributed, process-based model of an urban runoff energy balance. To do so, this study investigated the stormwater runoff heat transfer in a case study area that included urban parking lot (2,500 m2), roadway (1,100 m2), and roof (175 m2) subcatchments in Milwaukee, WI . Temperatures, runoff, and atmospheric data were collected for each subcatchment from summer rainfall events via in-situ data loggers and aerial thermal imagery from a small unmanned aerial system (UAS). The utilization of aerial imagery from a small UAS allowed on-demand, high resolution (1 in) urban land surface temperature values. This data will be used to calibrate a deterministic, event-scale model that simulates urban heat and runoff dynamics during runoff using Python. Ultimately, this study will result in a novel approach to assimilate UAS data into a spatially distributed model to better understand the influence that man-made urban surfaces have on urban runoff temperature pollution.