Evaluation of a simple CFD model in a complex vegetated urban area during the 2015 Engineering-quad experiment at the University of Utah

In urban areas, vegetation cover has an important effect in modulating the urban heat island (UHI). For example, during the day, trees typically cool the air via evapotranspiration and shading thereby reducing energy consumption. Nonetheless, their effects are often neglected in microclimate wind modeling studies due to the lack of appropriately-resolved models, which include complex flow modifications resulting from vegetation. This study explores the effects of vegetation canopies on the alteration of urban microclimate in highly-vegetated areas. Here, we use a fast-response simple CFD model called Quick Urban Industrial Complex wind field modeling system (QUIC-URB), which uses empirical parameterization along with mass conservation to produce averaged three-dimensional wind fields. This model is used to assess and compare the diurnal cycle of the mean wind field around buildings and vegetation elements using experimental data from near-surface time-averaged wind measurements obtained during the 2015 engineering-quad experiment at the University of Utah. During this experiment, low-cost local energy-budget measurement stations (LEMS) were used to measure wind speed and direction. Three different test cases are investigated including: the no-vegetation canopy model, the default vegetation canopy model in QUIC-URB (that accounts for momentum damping with appropriate attenuation coefficients for different trees species), and a new vegetation model with new empirical parameterizations for the wake field based on the specifications of the vegetation covers in the urban area. Statistical comparisons indicate reduced modeling error when the dynamic effect of tree wakes are included. Specifically, the results indicate up to 40% improvement in the modeled wind speed and 15% improvement in mean error of wind direction by using vegetation models. Furthermore, there are other parameters that can intensify the impact of tree wakes such as leaf area index, trees species, and surrounding building configurations, all of which have a significant impact on the QUIC-URB results.