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1. Urban Heat Islets: Street Segments, Land Surface Temperatures, and Medical Emergencies During Heat Advisories

   https://doi.org/10.2105/AJPH.2020.305636  

   O’Brien, Daniel T.; Gridley, Brian; Trlica, Andrew; Wang, Jonathan A.; Shrivastava, Aatmesh (July 2020, American Journal of Public Health)

   null (Ed.)

   Objectives. To examine the relationships among environmental characteristics, temperature, and health outcomes during heat advisories at the geographic scale of street segments. Methods. We combined multiple data sets from Boston, Massachusetts, including remotely sensed measures of temperature and associated environmental characteristics (e.g., canopy cover), 911 dispatches for medical emergencies, daily weather conditions, and demographic and physical context from the American Community Survey and City of Boston Property Assessments. We used multilevel models to analyze the distribution of land surface temperature and elevated vulnerability during heat advisories across streets and neighborhoods. Results. A substantial proportion of variation in land surface temperature existed between streets within census tracts (38%), explained by canopy, impervious surface, and albedo. Streets with higher land surface temperature had a greater likelihood of medical emergencies during heat advisories relative to the frequency of medical emergencies during non–heat advisory periods. There was no independent effect of the average land surface temperature of the census tract. Conclusions. The relationships among environmental characteristics, temperature, and health outcomes operate at the spatial scale of the street segment, calling for more geographically precise analysis and intervention. 

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2. Equitable community resilience: The case of Winter Storm Uri in Texas

   Ali Nejat Laura Solitare Edward Pettitt Hamed Mohsenian-Rad (July 2022, International journal of disaster risk reduction)

   Community resilience in the face of natural hazards involves both a community's sensitivity to disaster and its potential to bounce back. A failure to integrate equity into resilience considerations results in unequal recovery and disproportionate impacts on vulnerable populations, which has long been a concern in the United States. This research investigated aspects of equity related to community resilience in the aftermath of Winter Storm Uri in Texas which led to extended power outages for more than 4 million households. County-level outage/recovery data was analyzed to explore potential significant links between various county attributes and their share of the outages during the recovery/restoration phase. Next, satellite imagery was used to examine data at a much higher geographical resolution focusing on census tracts in the city of Houston. The goal was to use computer vision to extract the extent of outages within census tracts and investigate their linkages to census tracts attributes. Results from various statistical procedures revealed statistically significant negative associations between counties' percentage of non-Hispanic whites and median household income with the ratio of outages. Additionally, at census tract level, variables including percentages of linguistically isolated population and public transport users exhibited positive associations with the group of census tracts that were affected by the outage as detected by computer vision analysis. Informed by these results, engineering solutions such as the applicability of grid modernization technologies, together with distributed and renewable energy resources, when controlled for the region's topographical characteristics, are proposed to enhance equitable power grid resiliency in the face of natural hazards. 

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3. Recognizing flood exposure inequities across flood frequencies

   https://doi.org/10.1016/j.ancene.2023.100371  

   Selsor, Haley; Bledsoe, Brian P.; Lammers, Roderick (June 2023, Anthropocene)

   Urban flooding is a growing threat due to land use and climate change. Vulnerable populations tend to have greater exposure to flooding as a result of historical societal and institutional processes. Most flood vulnerability studies focus on a single large flood, neglecting the impact of small, frequent floods. Therefore, there is a need to investigate inequitable flood exposure across a range of event magnitudes and frequencies. To explore this question, we develop a novel score of inequitable flood risk by defining risk as a function of frequency, exposure, and vulnerability. This analysis combines high-resolution, parcel-scale compounded fluvial and pluvial flood data with census data at the census block group scale. We focus on six census tracts within Athens-Clarke County, Georgia that are highly developed with diverse populations. We define vulnerable populations as non-Hispanic Black, Hispanic, and households under the poverty level and use dasymetric mapping techniques to calculate the over-representation of these populations in flood zones. Inequitable risks at each census tract (approximately neighborhood scale) were estimated for multiple (e.g., 5-, 10-, 20-, 50-, and 100-year) flood return periods. Results show that the relatively greatest flood risk inequities occur for the 10-year flood and not at the largest event. We also found that the size of inequity is dynamic, depending on the flood magnitude. Therefore, addressing a range of events including smaller, more frequent floods can increase equity and reveal opportunities that may be missed if only one event is considered. 

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4. Comparing land surface temperature and mean radiant temperature for urban heat mapping in Philadelphia

   https://doi.org/10.1016/j.uclim.2023.101615  

   Li, Xiaojiang; Chakraborty, TC; Wang, Guoqing (September 2023, Urban Climate)

   Many cities are experiencing more frequent extreme heat during hot summers. With the rise of global temperature, the thermal comfort in urban areas become even worse. Quantitative information of the spatial distributions of urban heat has become increasingly important for resilience and adaptation to climate change in cities. This study compares satellite-derived land surface temperature (LST) and urban microclimate modeling-based mean radiant temperature (Tmrt) for mapping the urban heat distributions in Philadelphia, Pennsylvania, USA. The LST was estimated based on Landsat 8 thermal imagery with a spatial resolution of around 100 m, while the Tmrt was simulated based on high resolution LiDAR and national aerial imagery program multispectral aerial imageries with a spatial resolution of 1 m. Result shows that both LST and Tmrt show a similar general pattern of the urban heat across the study area, while the Tmrt presents much more details of the heat variations street by street and neighborhood by neighborhood. The LST tends to have a stronger relationship with the Tmrt on building roofs, which are usually not the place for human activities. This studyprovides evidence for choosing more appropriate metrics in urban heat-related studies. 

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5. Multi‐Sensor Approach for High Space and Time Resolution Land Surface Temperature

   https://doi.org/10.1029/2021EA001842  

   Desai, Ankur R.; Khan, Anam M.; Zheng, Ting; Paleri, Sreenath; Butterworth, Brian; Lee, Temple R.; Fisher, Joshua B.; Hulley, Glynn; Kleynhans, Tania; Gerace, Aaron; et al (October 2021, Earth and Space Science)

   Abstract Surface‐atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub‐grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub‐kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not continuously available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES‐16 and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station [ECOSTRESS]) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to independent observations from a network of 20 micrometeorological towers and piloted aircrafts in addition to Landsat‐based LST retrieval and drone‐based LST observed at one tower site. The downscaled 50‐m hourly LST showed good relationships with tower (r² = 0.79, RMSE = 3.5 K) and airborne (r² = 0.75, RMSE = 2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio‐temporal variation compared to a geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hot and cold spots across the landscape as evidenced by independent drone LST, with significant reduction in RMSE by 1.3 K. These results demonstrate a simple pathway for multi‐sensor retrieval of high space and time resolution LST. 

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