Modeling urban energy fluxes across Los Angeles

City-scale modeling of urban energy fluxes is important for characterizing urban microclimates, the urban heat island, and related human health effects. However, model accuracy is challenged by urban surface heterogeneity and data limitations, which make it difficult to quantify city-scale urban surface variability. This study modeled urban flux variability for 2,100 neighborhoods (each 1 km²) across Los Angeles, CA. We used the Surface Urban Energy and Water Balance Scheme (SUEWS: Jarvi et al., 2011), with localized parameters for urban surface materials, plant type, albedo, and structural variability extracted from a suite of remote sensing imagery (airborne hyperspectral and LiDAR) and GIS data. Model outputs were validated using airborne MASTER sensor LST imagery (36 m resolution), and were then used to investigate flux variability across neighborhoods having different material and structural composition.

Modeled sensible heat fluxes were linearly correlated with measured LST at the neighborhood scale (r² = 0.37). Collectively, neighborhoods had high diversity in surface characteristics, including vegetation fraction (21.6% +/- 11.3%, mean ± 1 SD), paved fraction (46% +/- 10%), tree to building height ratio (1.9 +/- 0.5), and roughness length (0.61 m +/- 0.4 m). Latent and sensible heat fluxes showed significant levels of variability across Los Angeles, with modeled latent heat fluxes increasing by 10 Wm^-2 for every 1% increase in vegetation cover. Further assessment across neighborhoods differing in age revealed an age-dependent, non-linear pattern of vegetation cover and related temperature and flux variability. These results suggest that city-scale urban energy flux analyses can be augmented by similarly large-scale remote sensing inputs, indicating potential for improved understanding of urban microclimates with imagery available from planned satellite missions including NASA's Hyperspectral Infrared Imager.