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Urban land use land cover (LULC) change raises ambient temperature and modifies atmospheric moisture, which increases heat-related health risks in cities. Greenspace and bluespace commonly coexist in urban landscapes and are nature-based heat mitigation strategies. Yet, their interactive effects on urban thermal environments are rarely assessed and it remains unclear how extreme heat events (EHEs) affect their ability to regulate human thermal comfort. Using multi-year observations from a dense urban observational network in Madison, WI, we found that green and blue spaces jointly modify the intraurban spatiotemporal variability of temperature and humidity, and the resultant effects on thermal comfort show diurnal and seasonal asymmetry. Greenspace is more effective at cooling throughout the year, particularly at night. Accelerated cooling efficiency is found in areas with dominant greenspace coverage and little co-influence from bluespace. The thermal comfort benefit due to greenspaces can be offset by bluespaces because of intensified nighttime warming and humidifying effects during the warm months, although a weak daytime cooling of bluespace is observed. EHEs enhance bluespace cooling, but the overall joint thermal regulation remains the same due to the enhanced moisture effect. Our findings suggest that diverse outcomes of green and blue spaces cross multiple temporal scales should be holistically assessed in urban planning. The analysis framework based on generalized additive models is robust and transferable to other cities and applications to disentangle the nonlinear co-influences of different drivers of urban environmental phenomena.

Despite the importance of urban trees’ surface temperature in assessing micro-climate interactions between trees and the surrounding environment, their diurnal evolution has been largely understudied at a city-wide scale due to a lack of effective thermal observations. By downscaling ECOSTRESS land surface temperature imaginary over New York City, we provide the first diurnal analysis of city-scale canopy temperature. Research reveals a remarkable spatial variation of the canopy temperature during daytime up to 5.6 K (standard deviation, STD), while the nighttime STD remains low at 1.7 K. Further, our analysis shows that the greenspace coverage and distance to bluespaces play an important role in cooling the local canopy during daytime, explaining 25.0–41.1% of daytime spatial variation of canopy temperatures while surrounding buildings modulate canopy temperature asymmetrically diurnally: reduced daytime warming and reduced nocturnal cooling. Built on space-borne observations and a flexible yet robust statistical method, our research design can be easily transferable to explore urban trees’ response to local climate across cities, highlighting the potentials of advancing the science and technologies for urban forest management.

Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies’ programming. However, state of the art models often rely on proprietary data and/or deep or transfer learning methods whose underlying mechanics may be challenging to interpret. We demonstrate how interpretable random forest models can produce estimates of a set of (potentially correlated) malnutrition and poverty prevalence measures using free, open access, regularly updated, georeferenced data. We demonstrate two use cases: contemporaneous prediction, which might be used for poverty mapping, geographic targeting, or monitoring and evaluation tasks, and a sequential nowcasting task that can inform early warning systems. Applied to data from 11 low and lower-middle income countries, we find predictive accuracy broadly comparable for both tasks to prior studies that use proprietary data and/or deep or transfer learning methods.

A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed Is and Ic trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2003–2020), here we find that the magnitude of the global mean Is trend (0.19 ± 0.006°C/decade, mean ± SE) for 5,643 cities worldwide is nearly six‐times the corresponding Ic trend (0.03 ± 0.002°C/decade) during the day, while the former (0.06 ± 0.004°C/decade) is double the latter (0.03 ± 0.002°C/decade) at night. Variable importance scores indicate that global daytime Is trend is slightly more controlled by surface property, while background climate plays a more dominant role in regulating global daytime Ic trend. At night, both global Is and Ic trends are mainly controlled by background climate. Plain Language Summary Surface and canopy urban heat islands (surface and canopy UHIs, termed Is and Ic) are two major UHI types. These two counterparts are both related to urban population heat exposure and have long been a focus of urban climate research. However, the differences in the trends and major determinants of Is and Ic over global cities remain largely unclear. Based on spatially continuous land surface temperature and surface air temperature observations from 2003 to 2020, we find that the global mean Is trends are about 6.3 times and 2 times the Ic trends during the day and at night, respectively. During the day, the global Is trend is more regulated by surface property than by background climate, and vice versa for global Ic trend. At night, both the global Is and Ic trends are mainly regulated by background climate. These findings are important for better understanding global urban climate change and informing heat mitigation strategies. Key Points The global Is trend is six‐fold and twofold larger than the Ic trend during the day and at night, respectively During the day, global Is trend is slightly more controlled by surface property, yet background climate plays a dominant role in Ic trend At night, both global Is and Ic trends are more regulated by background climate

As urbanization progresses under a changing climate, urban populations face increasing threats from chronically higher heat exposures and more frequent extreme heat events. Understanding the complex urban thermal exposure patterns becomes crucial for effective heat risk management. The spatial advantage of satellite thermal observations positions surface urban heat islands (SUHI) as a primary measure for such applications at the city scale. However, satellite‐inherent biases pose considerable uncertainties. To improve the representation of human‐relevant heat exposure, this study proposes a simple but effective satellite‐based measure– ground urban heat island (GUHI), focusing solely on radiant temperatures from urban ground elements. Leveraging ECOSTRESS land surface temperature product and radiation‐based statistical downscaling, diurnally representative GUHIs were evaluated over NYC. The findings reveal that overall GUHI is consistently warmer than SUHI diurnally. However, GUHI exhibits complex spatial contrasts with SUHI, primarily influenced by vegetation coverage. Various indicators associated with urban structures and materials were examined, showing important but dissimilar roles in shaping the spatial dynamics of GUHI and SUHI. This study highlights the value of satellite thermal observations compared to air temperature while addressing uncertainties in widely adopted practices of using them. By improving the depiction of human‐related urban heat patterns from Earth observations, this research offers valuable insight and more reliable measures to address the urgent requirements for urban heat risk management globally. Plain Language Summary As cities grow under a changing climate, people in urban areas face more heat, putting them at risk. To protect them, it's crucial to understand where and when heat is most intense in cities. Direct satellite measurements help show surface urban heat islands (SUHI) that are widely used for mapping hot spots to identify higher heat risks and vulnerable communities. However, this method can be biased. This study suggests a new way to measure heat in cities using satellites that only consider the heat coming from the ground in urban areas, called ground urban heat islands (GUHI). By using data from the ECOSTRESS aboard the International Space Station and statistical approaches, we looked at how hot urban ground was throughout the day in New York City. We found that overall, GUHI was consistently hotter than SUHI throughout the day. However, heat patterns depicted by GUHI were influenced by urban surface properties in complex ways and were often different from SUHI at the local scale. This research shows that using satellite data to measure heat can give us better insights than air temperature for spatial applications, and this study offers more reliable ways to support heat risk management in cities worldwide. Key Points Ground urban heat island (GUHI) is proposed to improve satellite thermal observations applied for heat risk management GUHIs are warmer and show complex spatial contrasts with surface urban heat islands Urban materials and structures contribute differently to diurnal dynamics of GUHIs

The spatiotemporal characteristics of air temperature and humidity mediated by urban bluespace are investigated using a combination of dense network of climatological observations in a medium-sized US city, computational fluid dynamics, and analytical modeling approaches. Both numerical simulation and observational results show that the rate of change of hourly averaged air temperature and humidity at 3.5 m over urban areas peaks 2 h after sunset, while it decreases with time monotonically over greenspace, indicating different impacts due to presence of urban lakes. The apparent temperature decreases with distance to lakes in urban area due to higher near-shore humidity. This highlights that urban lakes located near city center can deteriorate the nighttime cooling effects due to elevated humidity. Finally, two analytical models are presented to explain the connection between the surface and air temperature as well as the spatial variation of air temperature and humidity adjacent to the urban lakes. These simplified models with parameters being inferred from the network of measurements have reasonably good performance compared to the observations. Compared to other sophisticated numerical simulations, these analytical models offer an alternative means that is easily accessible for evaluating the efficacy of bluespace on urban nocturnal cooling.

Rapid urban development often comes at the cost of significant vegetation loss, and the loss of urban trees is a particularly concerning issue. Many cities have recognized the crucial role of tree canopy cover in addressing a range of pressing environmental challenges, and have set goals in their development plans. However, the lack of resources for monitoring its spatiotemporal changes directly undermines efforts of cities to balance urban growth with environmental sustainability. We developed high-resolution biennial tree canopy maps (2013–2021) for the Orlando metropolitan area using National Agricultural Imagery Program (NAIP) orthoimagery. Our approach addresses challenges such as sensor inconsistencies, shadows, variable observation times, and environmental factors. Extensive ground-truth validation demonstrates 78%–89% accuracy in tree detection, outperforming existing products. This dataset provides the crucial link between planning and action, and supports urban forest management and has broader applications in urban planning, environmental and public health monitoring, and education programs.

The effects of biophysical and meteorological factors on land surface temperature (LST) have been well studied in previous research. However, less attention has been paid to examine how building materials influence the magnitude of LST within an urban environment. This study investigates the interaction of biophysical and building wall materials to influence LST in Harris County, Texas, USA using multiple stepwise linear regression analyses and neighborhood analysis. Working at 1 km grid resolution, LST data is related to impervious surface fraction, albedo, distance to water bodies, and seven major wall types. Ten years of aggregated MODIS (Moderate Resolution Imaging Spectroradiometer) daily LST products were used to calculate the mean LST in January and August for daytime and nighttime conditions. Harris County 2010 parcel level building property data were used to create composition characteristics of the building wall types. Our results demonstrate that both biophysical and building wall characteristics significantly influence the spatiotemporal variations of LST. However, biophysical factors are the dominant explaining factors compared to building wall materials. Impervious surface fraction is the most significant variable to explain the variation of LST, and has positive effects on LST. In contrast, high albedo materials and the presence of open water bodies significantly affect LST and are good candidate variables to mitigate the heat island effect. Furthermore, the building wall variables all increase LST for both daytime and nighttime, but different wall materials have various effects on LST. Brick/veneer and frame/concrete block are the two dominant wall types in Harris County and tend to generate higher LST. These results demonstrate how building materials, in combination with biophysical factors, can be used to mitigate neighborhood-scale LST. This methodology works reasonably well for Houston, but is likely to be more effective in higher density urban settings.

Urban areas are known to modify the spatial pattern of precipitation climatology. Existing observational evidence suggests that precipitation can be enhanced downwind of a city. Among the proposed mechanisms, the thermodynamic and aerodynamic processes in the urban lower atmosphere interact with the meteorological conditions and can play a key role in determining the resulting precipitation patterns. In addition, these processes are influenced by urban form, such as the impervious surface extent. This study aims to unravel how different urban forms impact the spatial patterns of precipitation climatology under different meteorological conditions. We use the Multi‐Radar Multi‐Sensor quantitative precipitation estimation data products and analyze the hourly precipitation maps for 27 selected cities across the continental United States from the years 2015–2021 summer months. Results show that about 80% of the studied cities exhibit a statistically significant downwind enhancement of precipitation. Additionally, we find that the precipitation pattern tends to be more spatially clustered in intensity under higher wind speed; the location of radial precipitation maxima is located closer to the city center under low background winds but shifts downwind under high wind conditions. The magnitude of downwind precipitation enhancement is highly dependent on wind directions and is positively correlated with the city size for the south, southwest, and west directions. This study presents observational evidence through a cross‐city analysis that the urban precipitation pattern can be influenced by the urban modification of atmospheric processes, providing insight into the mechanistic link between future urban land‐use change and hydroclimates. Plain Language Summary Previous studies have shown that cities can influence the spatial rainfall patterns, and one of the strongest influences is that the precipitation tends to increase over downwind of city areas. The goal of this study is to understand how different urban forms impact rainfall spatial patterns under different weather conditions. We analyze the hourly precipitation accumulation data from Multi‐Radar Multi‐Sensor for a selected set of 25 cities across the continental United States from the years 2015–2021 summer months. The results indicate that more than 80% of the studied cities have a significant increase of rainfall in downwind regions. In addition, the rainfall spatial patterns have different characteristics with varying meteorological conditions such as precipitation intensities, wind speeds, and wind directions in terms of the location of rainfall maxima, the magnitude of downwind enhancement. Key Points 22 out of 27 cities show statistically significant downwind enhancement of climatological precipitation The precipitation patterns exhibit different spatial characteristics with varying meteorological conditions Downwind enhancement factor is positively correlated with city size under dominant wind direction

Sustained increase in atmospheric CO2 is strongly coupled with rising temperature and persistent droughts. While elevated CO2 promotes photosynthesis and growth of vegetation, drier and warmer climate can potentially negate this benefit, complicating the prediction of future terrestrial carbon dynamics. Manipulative studies such as free air CO2 enrichment (FACE) experiments have been useful for studying the joint effect of global change factors on vegetation growth; however, their results do not easily transfer to natural ecosystems partly due to their short-duration nature and limited consideration of climatic gradients and potential confounding factors, such as O3. Urban environments serve as a useful small-scale analogy of future climate at least in reference to CO2 and temperature enhancements. Here, we develop a data-driven approach using urban environments as test beds for revealing the joint effect of changing temperature and CO2 on vegetation response to drought. Using 75 urban-rural paired plots from three climate zones over the conterminous United States (CONUS), we find vegetation in urban areas exhibits a much stronger resistance to drought than in rural areas. Statistical analysis suggests the drought resistance enhancement of urban vegetation across CONUS is attributed to rising temperature (with a partial correlation coefficient of 0.36) and CO2 (with a partial correlation coefficient of 0.31) and reduced O3 concentration (with a partial correlation coefficient of −0.12) in cities. The controlling factor(s) responsible for urban-rural differences in drought resistance of vegetation vary across climate regions, such as surface O3 gradients in the arid climate, and surface CO2 and O3 gradients in the temperate and continental climates. Thus, our study provides new observational insights on the impacts of competing factors on vegetation growth at a large scale, and ultimately, helps reduce uncertainties in understanding terrestrial carbon dynamics.