nasa-gesdisc/es-publications-researchareas huggingface dataset:
Widespread Race and Class Disparities in Surface Urban Heat Extremes (2021)

Here we use remotely sensed land surface temperature measurements to explore the distribution of the United States' urban heating burden, both at high resolution (within cities or counties) and at scale (across the whole contiguous United States). While a rich literature has documented neighborhood-level disparities in urban heat exposures in individual cities, data constraints have precluded comparisons across locations. Here, drawing on urban temperature anomalies during extreme summer surface temperature events from all 1,056 US counties with more than 10 developed census tracts, we find that the poorest tracts (and those with lowest average education levels) within a county are significantly hotter than the richest (and more educated) neighborhoods for 76% of these counties (54% for education); we also find that neighborhoods with higher Black, Hispanic, and Asian population shares are hotter than the more White, non-Hispanic areas in each county. This holds in counties with both large and small spreads in these population shares, and for 71% of all counties the significant racial urban heat disparities persist even when adjusting for income. Although individual locations have different histories that have contributed to race- and class-based geographies, we find that the physical features of the urban environments driving these surface heat exposure gradients are fairly uniform across the country. Systematically, the disproportionate heat surface exposures faced by minority communities are due to more built-up neighborhoods, less vegetation, and—to a lesser extent—higher population density.

Research areas: Heat
Context sensitivity of surface urban heat island at the local and regional scales (2021)

In this study we analysed the multi-annual (20022011) average summer surface urban heat island (SUHI) intensity of the 5000 largest urban clusters in Europe. We investigated its relationship with a proposed Gravitational Urban Morphology (GUM) index that can capture the local context sensitivity of SUHI. The GUM index was found to be an effective predictor of SUHI intensity. Together with other urban factors we built different multivariate linear regression models and a climate space based geographically weighted regression (GWR) model that can better predict SUHI intensity. As the GWR model captures the variation of influence from different urban factors on SUHI, it considerably outperformed linear models in predicting SUHI intensity in terms of R2 and other statistical criteria. By investigating the variation of GWR coefficients against background climate factors, we further built a nonlinear regression model that takes into account the sensitivity of SUHI to regional climate context. The nonlinear model showed comparable performance to that of the GWR model and it prevailed against all the linear models. Our work underlines the potential of SUHI reduction through optimising urban morphology, as well as the importance of integrating future urbanisation and climate change into the implementation of urban heat mitigation strategies.

Research areas: Heat
LCZ scheme for assessing Urban Heat Island intensity in a complex urban area (Beirut, Lebanon) (2021)

Thermal remote sensing has become widely used to monitor and study Urban Heat Island (UHI) by the use of satellite Land Surface Temperature (LST) data, and the Local Climate Zone (LCZ) scheme has established itself as the standard for assessing Urban Heat Island Intensity (UHII). A lot of research on LST-LCZ relationship has been done, but mainly in flat terrain. Some studies on UHI in mountainous areas have been conducted, but most of them considered the combined effect of land use/land cover and topography on temperature. To the best of our knowledge no study has been carried out on the LST-LCZ relationship in a complex topography area after removing the topography effect. This paper presents a methodology for assessing the impact of the urban structure on UHII in Beirut, a coastal city with heterogenous urban morphology and complex topography. It was concluded that the main urban variables which contribute to the spatial variability of topographically normalized LST are, in decreasing order of relative importance, the built-up ratio, the pervious surface ratio, the buildings' mean height and mean surface area. The difference in average temperatures between high-rise densely built LCZs and mostly pervious zones exceeded two degrees at nighttime.

Research areas: Heat
A mechanistic assessment of urban heat island intensities and drivers across climates (2022)

The urban heat island effect (UHI) has been widely observed globally, causing climate, health, and energy impacts in cities. The UHI intensities have been found to largely depend on background climate and the properties of the urban fabric. Yet, a complete mechanistic understanding of how UHIs develop at a global scale is still missing. Using an urban ecohydrological and land-surface model (urban Tethys-Chloris) in combination with multi-source remote sensing data, we performed simulations for 49 large urban clusters across the Northern Hemisphere in 20092019 and analysed how surface and canopy air UHIs (SUHI and CUHI, respectively) develop during day and night. Biophysical drivers triggering the development of SUHIs and CUHIs have similar dependencies on background climate, but with different magnitudes. In humid regions daytime UHIs can be largely explained by the urban-rural difference in evapotranspiration, whereas heat convection and conduction are important in arid areas. Plant irrigation can largely promote daytime urban evapotranspiration only in arid and semi-arid climates. During night, heat conduction from the urban fabric to the environment creates large UHIs mostly in warm arid regions. Overall, this study presents a mechanistic quantification of how UHIs develop worldwide and proposes viable solutions for sustainable climate-sensitive mitigation strategies.

Research areas: Heat
Projections of Human Exposure to Dangerous Heat in African Cities Under Multiple Socioeconomic and Climate Scenarios (2019)

Human exposure to dangerous heat, driven by climatic and demographic changes, is increasing worldwide. Being located in hot regions and showing high rates of urban population growth, African cities appear particularly likely to face significantly increased exposure to dangerous heat in the coming decades. We combined projections of urban population under five socioeconomic scenarios—shared socioeconomic pathways—with projections of apparent temperature under three representative concentration pathways in order to explore future exposure to dangerous heat across 173 large African cities. Employing multiple shared socioeconomic pathway and representative concentration pathway combinations, we demonstrated that the aggregate exposure in African cities will increase by a multiple of 20-52, reaching 86-217 billion person-days per year by the 2090s, depending on the scenario. The most exposed cities are located in Western and Central Africa, although several Eastern African cities showed an increase of more than 2,000 times the current level by the 2090s, due to the emergence of dangerous heat conditions combined with steady urban population growth. In most cases, we found future exposure to be predominantly driven by changes in population alone or by concurrent changes in climate and population, with the influence of changes in climate alone being minimal. We also demonstrated that shifting from a high to a low urban population growth pathway leads to a slightly greater reduction in aggregate exposure than shifting from a high to a low emissions pathway (51% vs. 48%). This emphasizes the critical role that socioeconomic development plays in shaping heat-related health challenges in African cities.

Research areas: Heat
UrbanRural Gradient in Urban Heat Island Variations Responsive to LargeScale Human Activity Changes During Chinese New Year Holiday (2022)

Large-scale human activity changes in megacities during Chinese New Year (CNY) are believed to significantly affect urban heat islands (UHIs). However, the urban-rural gradient in UHI variations responsive to human activity changes in cities remains largely unclear. Using in-situ surface air temperature obtained from a meteorological network that includes 3000-plus stations, we show that the mean UHI intensity (UHII) in 31 Chinese capitals is 0.52 ± 0.23 K during the CNY holiday and 0.77 ± 0.29 K in the reference period, indicating a UHII reduction of 0.25 ± 0.20 K during the holiday. The reduced UHII decreased more from city core (0.54 K) to city periphery (0.071 K). We find that these UHII reductions were larger at night than during the day and were larger in northern subtropical and warm temperate climates than in other climates. These UHII reductions were mainly attributable to the decline in anthropogenic heat release.

Research areas: Heat
An Approach to Characterizing the Spatial Pattern and Scale of Regional Heat Islands over Urban Agglomerations (2022)

Great difficulty in characterizing the spatial pattern and scale of regional surface urban heat islands (RSUHIs) over urban agglomerations is mainly attributed to the RSUHI extent exceeding urban boundaries and strong variations in the background climate state and land cover. A new method that successfully addresses these problems by applying the spatial similarity of the land surface temperature (LST) annual cycle in urban agglomerations is proposed to characterize the RSUHI spatial pattern and scale in the Yangtze River Delta of China. This method has good adaptability to complex terrain and climate variations and simultaneously identifies the RSUHI spatial pattern and scale. The RSUHI spatial extent averaged 3.7 × 10<SUP>4</SUP> km<SUP>2</SUP> in 2003-2020, which is 1.5-2.0 times the urban extent, and had a fluctuating but increasing tendency. Rapid urbanization and the spatial pattern-shifting LST anomalies were mainly responsible for the fluctuating RSUHI extent during 2003-2020.

Research areas: Heat
Population-Based Disparities in U.S. Urban Heat Exposure from 2003 to 2018 (2022)

Previous studies have shown, in the United States (U.S.), that communities of color are exposed to significantly higher temperatures in urban environments than complementary White populations. Studies highlighting this disparity have generally been cross-sectional and are therefore snapshots in time. Using surface urban heat island (SUHI) intensity data, U.S. Census 2020 population counts, and a measure of residential segregation, this study performs a comparative analysis between census tracts identified as prevalent for White, Black, Hispanic and Asian populations and their thermal exposure from 2003 to 2018. The analysis concentrates on the top 200 most populous U.S. cities. SUHI intensity is shown to be increasing on average through time for the examined tracts. However, based on raw observations the increase is only statistically significant for White and Black prevalent census tracts. There is a 1.25 K to ~2.00 K higher degree of thermal exposure on average for communities of color relative to White prevalent areas. When examined on an inter-city basis, White and Black prevalent tracts had the largest disparity, as measured by SUHI intensity, in New Orleans, LA, by <6.00 K. Hispanic (>7.00 K) and Asian (<6.75 K) prevalent tracts were greatest in intensity in San Jose, CA. To further explore temporal patterns, two models were developed using a Bayesian hierarchical spatial temporal framework. One models the effect of varying the percentages of each population group relative to SUHI intensity within all examined tracts. Increases in percentages of Black, Hispanic, and Asian populations contributed to statistically significant increases in SUHI intensity. White increases in population percentage witnessed a lowering of SUHI intensity. Throughout all modeled tracts, there is a statistically significant 0.01 K per year average increase in SUHI intensity. A second model tests the effect of residential segregation on thermal inequity across all examined cities. Residential segregation, indeed, has a statistically significant positive association with SUHI intensity based on this portion of the analysis. Similarly, there is a statistically significant 0.01 K increase in average SUHI intensity per year for all cities. Results from this study can be used to guide and prioritize intervention strategies and further urgency related to social, climatic, and environmental justice concerns.

Research areas: Heat
Spatial patterns and driving factors of surface urban heat island intensity: A comparative study for two agriculture-dominated regions in China and the USA (2019)

Urban heat island (UHI) effect causes tremendous ecological and environmental consequences. A better understanding of its spatial pattern and driving factors is highly required for designing effective mitigation strategies and adaptation measures. This study compared spatial patterns of surface UHI intensity (SUHII) and their driving factors between two agriculture-dominated regions (i.e., BeijingTianjinHebei (BTH), China and IllinoisIndianaOhio (IIO), the USA) based on the MODIS land surface temperature dataset. The results showed that: (1) IIO had significantly stronger daytime SUHII than BTH, while the nighttime SUHII showed an opposite phenomenon. (2) SUHIIs of different time were positively correlated in IIO, but the winter daytime SUHII was negatively correlated with the nighttime SUHII in BTH. (3) Spatial variations of summer SUHII were better predicted in IIO than that in BTH, but the opposite result was found for winter SUHII. (4) Some regression coefficients of the multiple linear regression models when modeling winter daytime SUHII were opposite between IIO and BTH. Findings of this study indicated different dominant surface energy processes in controlling the spatial pattern of SUHII in these two regions of different countries and recommended varied adaptation measures and mitigation strategies.

Research areas: Heat
Exploring surface urban heat island (SUHI) intensity and its implications based on urban 3D neighborhood metrics: An investigation of 57 Chinese cities (2022)

Excessive urban temperature exerts a substantially negative impact on urban sustainability. Three-dimensional (3D) landscapes have a great impact on urban thermal environments, while their heat conditions and driving factors still remain unclear. This study mapped urban 3D neighborhoods and their associated SUHI (surface urban heat island) intensities in summer daytime across 57 Chinese cities, and then explored their relationships, driving factors as well as implications. Nine categories of urban 3D neighborhoods existed in Chinese cities and the 3D neighborhood of High Density & Medium Rise (HDMR) contributed the largest share of urban areas. The distribution of 3D neighborhoods varied among cities due to their distinct natural and economic traits. The average SUHI intensity can amount to 4.27 C across all Chinese 3D neighborhoods. High Density & Low Rise (HDLR) and HDMR presented higher SUHI intensities than other 3D neighborhoods in China. Urban green space (UGI) and building height (BH) had great influences on SUHI intensities. The relative contribution of UGI decreased with the increase of building density and building height, but BH presented the opposite trend. The interaction of urban 3D landscapes and function zones led to highly complicated urban thermal environments, with higher SUHI intensities in industrial zones. Besides, the SUHI intensities of 3D neighborhoods presented great diurnal and seasonal variations, with higher SUHI intensities in HDHR and HDMR at nighttime in winter and summer. What's more, urban residents may suffer unequal heat risk inside cities due to the deviations of SUHI intensities among different 3D neighborhoods. It could be a highly effective way to mitigate SUHI effects in cities by increasing urban greening and improving urban ventilation.

Research areas: Heat
Downwind footprint of an urban heat island on air and lake temperatures (2018)

The urban heat island (UHI) effect was first documented ~200 years ago, making it the longest recognized anthropogenic effect on climate. Although anomalous heating in cities has been meticulously characterized, less is known about how the UHI affects surrounding regions. It is hypothesized that downwind of cities a heat plume forms due to the advection of urban heat. This heat transport may have impacts beyond heating of the surface, such as disrupting atmospheric convection and influencing boundary layer structure, which influences weather, air quality, and human health. Here, a lagrangian atmospheric transport model, forced with archived data from a numerical weather model, is used to generate a three-dimensional map of an urban heat plume for a major city, Chicago. We document significant heating 100200 m above the surface and 70 km downwind of the city. Over Lake Michigan, the scale of the plume is truncated nearly in half (~40 km), suggesting the lake is acting as a sink for the exported urban heat. Using satellite lake surface temperatures, we observed a disruption of the diurnal pattern of lake temperature beneath the plume, which supports a possible role of the lake in absorbing the heat plume. The results provide unique quasi-observational evidence for a significant footprint of cities on regional atmospheric structure and potentially on adjacent aquatic bodies.

Research areas: Heat
Study on the Spatial Pattern of an Extreme Heat Event by Remote Sensing: (2020)

The intensity and frequency of extreme heat events are increasing globally, which has a great impact on resident health, social life, and ecosystems. Detailed knowledge of the spatial heat pattern during extreme heat events is important for coping with heat disasters. This study aimed to monitor the characteristics of the spatial pattern during the 2013 heat wave in the Yangtze River Delta (YRD), China, based on the remote sensing estimated gridded air temperature (Ta). Based on the land surface temperature (Ts), normalized difference vegetation index (NDVI), built-up area, and elevation derived from multi-source satellite data, the daily maximum air temperature (Ta_max) during the heat wave was mapped by the random forest (RF) algorithm. Based on the remotely sensed Ta, heat intensity index (HII) was calculated to measure the spatial pattern of heat during this heat wave. Results indicated that most areas in the YRD suffered from extreme heat, and the heat pattern also exhibited obvious spatial heterogeneity. Cities located in the Taihu Plain and the Hangjiahu Plain generally had high HII values. The northern plain in the YRD showed relatively lower HII values, and mountains in the southern YRD showed the lowest HII values. Heat proportion index (HPI) was calculated to qualify the overall heat intensity of each city in the YRD. Wuxi, Changzhou, and Shanghai showed the highest HPI values, indicating that the overall heat intensities in these cities were higher than others. Yancheng, Zhoushan, and Anqing ranked last. This study provides a good reference for understanding the pattern of heat during heat waves in the YRD, which is valuable for heat wave disaster prevention.

Research areas: Heat
Magnitude of urban heat islands largely explained by climate and population (2019)

Urban heat islands (UHIs) exacerbate the risk of heat-related mortality associated with global climate change. The intensity of UHIs varies with population size and mean annual precipitation, but a unifying explanation for this variation is lacking, and there are no geographically targeted guidelines for heat mitigation. Here we analyse summertime differences between urban and rural surface temperatures (Ts) worldwide and find a nonlinear increase in Ts with precipitation that is controlled by water or energy limitations on evapotranspiration and that modulates the scaling of Ts with city size. We introduce a coarse-grained model that links population, background climate, and UHI intensity, and show that urbanrural differences in evapotranspiration and convection efficiency are the main determinants of warming. The direct implication of these nonlinearities is that mitigation strategies aimed at increasing green cover and albedo are more efficient in dry regions, whereas the challenge of cooling tropical cities will require innovative solutions.

Research areas: Heat
Quantifying the response of surface urban heat island to urban greening in global north megacities (2021)

Urban heat island, a phenomenon that urban temperature is higher than the rural area nearby, affects directly citizens' human health and well-being. However, the cooling effect from urban green space (UGS) and the attribution of the different land processes to surface urban heat island intensity (SUHI) under different background climates remains unclear. The coarse-grained model was used to estimate summer SUHI in three different background climatic zones and for seven agglomerations (BTH, JP, LD, NAAC, NAGL, YZ, UQ). Results indicate that (1) the temperate zone had the highest daytime SUHI (010 C), while the arid zone has the lowest daytime SUHI (12 C). In both temperate and cold zone, the daytime SUHI was higher than the nighttime SUHI. The SUHI in downtown was higher (more than 2 C) than in the suburbs. (2) The increasing precipitation can enhance daytime SUHI while can weaken nighttime SUHI in all three climatic zones. The increasing temperature tends to enhance SUHI in both daytime and nighttime (exclude UQ). (3) The cooling effects of UGS in daytime SUHI were highly dependent on the background climate (cold > temperate > arid). (4) The nighttime SUHI could be effectively offset when UGSFs were greater than 0.48, 0.82, 0.97, 0.95 in NAAC, NAGL, YZ, and UQ. This article highlights the different feedback of urban green space to UHII and supports green infrastructure intervention as an effective means of reducing urban heat stress at urban agglomeration scales.

Research areas: Heat
Spatially non-stationary effect of underlying driving factors on surface urban heat islands in global major cities (2020)

Urban heat island (UHI) effect is among the most typical characteristics of urban climate. The analysis of surface UHI (SUHI) mechanisms has received the most extensive attention in the world. Here, we quantify the diurnal and seasonal SUHI intensity (SUHII) in global 419 major cities during the period 2003-2013. A geographically weighted regression (GWR) was established to assess the relationships between SUHII and several driving factors, and it further was compared to the ordinary least square (OLS) and stepwise multiple linear regression (SMLR) models. We show that GWR model has higher determination coefficient (R2) than OLS and SMLR models (Time: summer daytime, summer night, winter daytime and winter nighttime; GWR: 0.805, 0.458, 0.699 and 0.582; OLS: 0.732, 0.347, 0.473 and 0.320; SMLR: 0.732, 0.341, 0.468 and 0.316), indicating the spatially non-stationarity in the relationships. During the day, both vegetation activity and tree cover fraction have stronger cooling effect on SUHI in the summer of Asia. At night, there are stronger albedo effects on SUHI in the summer of Eastern Asia and Western North America and in the winter of Eastern Asia. Furthermore, temperature has stronger effect on daytime SUHI in Africa, Europe and South America in summer, and precipitation has stronger effect on nighttime SUHI in Africa and Europe in summer. Our results emphasize the spatial variation of the relationships between SUHII and relevant driving factors across global major cities, further indicating that the spatially non-stationary effect of driving factors on SUHII need to be considered in the future.

Research areas: Heat