Expansion of Major Urban Areas in the US Great Plains from 2000 to 2009 Using Satellite Scatterometer Data

A consistent dataset delineating and characterizing changes in urban environments will be valuable for socioeconomic and environmental research and for sustainable urban development. Remotely sensed data have been long used to map urban extent and infrastructure at various spatial and spectral resolutions. Although many datasets and approaches have been tried, there is not yet a universal way to map urban extents across the world. Here we combined a microwave scatterometer (QuikSCAT) dataset at ~1 km posting with percent impervious surface area (%ISA) data from the National Land Cover Dataset (NLCD) that was generated from Landsat data, and ambient population data from the LandScan product to characterize and quantify growth in nine major urban areas in the US Great Plains from 2000 to 2009. Nonparametric Mann-Kendall trend tests on backscatter time series from urban areas show significant expanding trends in eight of nine urban areas with p-values ranging 0.032 to 0.001. The sole exception is Houston, which has a substantial non-urban backscatter at the northeastern edge of the urban core. Strong power law scaling relationships between ambient population and either urban area or backscatter power (r2 of 0.96 in either model) with sub-linear exponents (β of 0.911 and 0.866, respectively) indicate urban areas become more compact with more vertical built-up structure than lateral expansion to accommodate the increased population. Increases in backscatter and %ISA datasets between 2001 and 2006 show agreement in both magnitude and direction for all urban areas except Minneapolis-St. Paul (MSP), likely due to the presence of many lakes and ponds throughout the MSP metropolitan area. We conclude discussing complexities in the backscatter data caused by large metal structures and rainfall

A WRF-UCM-SOLWEIG framework of 10m resolution to quantify the intra-day impact of urban features on thermal comfort

City-scale outdoor thermal comfort diagnostics are essential for understanding actual heat stress. However, previous research primarily focused on the street scale. Here, we present the WRF-UCM-SOLWEIG framework to achieve fine-grained thermal comfort mapping at the city scale. The background climate condition affecting thermal comfort is simulated by the Weather Research and Forecasting (WRF) model coupled with the urban canopy model (UCM) at a local-scale (500m). The most dominant factor, mean radiant temperature, is simulated using the Solar and Longwave Environmental Irradiance Geometry (SOLWEIG) model at the micro-scale (10m). The Universal Thermal Climate Index (UTCI) is calculated based on the mean radiant temperature and local climate parameters. The influence of different ground surface materials, buildings, and tree canopies is simulated in the SOLWEIG model using integrated urban morphological data. We applied this proposed framework to the city of Guangzhou, China, and investigated the intra-day variation in the impact of urban morphology during a heat wave period. Through statistical analysis, we found that the elevation in UTCI is primarily attributed to the increase in the fraction of impervious surface (ISF) during daytime, with a maximum correlation coefficient of 0.80. Tree canopy cover has a persistent cooling effect during the day. Implementing 40% of tree cover can reduce the daytime UTCI by 1.5 to 2.0 K. At nighttime, all urban features have a negligible contribution to outdoor thermal comfort. Overall, the established framework provides essential input data and references for studies and urban planners in the practice of urban (micro)climate diagnostics and planning