Vegetation is increasingly viewed as an important component of enhancing the heat resilience of cities. However, the amount of cooling vegetation can provide varies both geographically and temporally in ways that may depend on weather conditions. Within a city the distribution of vegetation can often exhibit patterns of inequality with high income and primarily white neighborhoods having the most vegetation and cooler temperatures, a clear indicator of inequitable human heat vulnerabilities. Here we test a landscape and ecophysiological framework for characterizing both the variation in vegetated cooling and the inequality in vegetation and outdoor surface temperature across 16 cities throughout the United States. Across all cities we identified a consistent negative relationship between satellite observations of vegetation greenness and surface temperature and this slope correlated with atmospheric aridity. Further, inequality in availability of vegetation and heat was non-linearly related to aridity, with a minimum of inequality over cities exhibiting intermediate aridity. We extended these results to evaluate potential future urban heat risk distributions by coupling our empirical ecophysiological model with a regional climate model projection. We showed that future conditions are anticipated to have higher rates of vegetative cooling, however, these changes cannot overcome overall climate warming effects. Consequently, social inequality decreases but only because vegetation has a reduced role in determining of neighborhood outdoor surface temperature.

Examining urban thermal environments has become a critical area of research spanning epidemiology, urban planning, and ecology. While traditional metrics like air temperature (Tair) and satellite-derived surface temperature dominate urban heat studies, these measures often fail to reflect how people actually experience thermal exposure intensity. More human-oriented metrics, such as mean radiant temperature (MRT), and the wet bulb globe temperature (WBGT), better capture this lived experience, particularly at locations where people are likely to encounter outdoor heat, such as bus stops. Human demographics further complicate heat exposure, as access to cooling resources like trees and greenspaces can vary by neighborhood income. Our study addresses these complications by collecting thermal data across 60 commuting locations in Denver, Colorado in the summer. We evaluate (1) the extent to which more human-oriented metrics capture thermal exposure compared to Tair and LST, and (2) how heat exposure varies by neighborhood income levels. We observed that bus stops in low-income neighborhoods had an MRT increase 2.8 degrees C compared wealthier neighborhoods, and that income-driven differences in MRT and WBGT strongly depended on bus stop aspect. After accounting for solar orientation, differences in MRT increased to as much as 6.3 degrees C at north-facing stops. Our results suggest tree canopy shade explains some observed heat exposure patterns, with south facing bus stops seeing a MRT and WBGT decrease of 0.42 degrees C and 0.11 degrees C from a percent increase in tree canopy. Interestingly, depending on bus stop aspect, nearby buildings can increase MRT and WBGT (facing east), or decrease MRT and WBGT (facing south) If planners aim to address this issue, consideration of bus stops, and land covers configuration may help.

Weather and climate variability increasingly shape urban travel behavior, yet the short-term temporal dynamics and contextual modifiers of weather-transit relationships remain poorly understood. We analyzed system-wide hourly bus ridership in the Denver metropolitan area from June 2022 through September 2023 using fixed-effects negative binomial distributed lag nonlinear models with lags up to 24 h. Nonlinear exposure-response and lag-response functions were specified for hourly Universal Thermal Climate Index (UTCI) and precipitation, with additional indicators for daily ozone exceedance and wildfire smoke. Models adjust for hour-of-day, day-of-week, and month-year fixed effects, with standard errors clustered by day. Stratified analyses assess heterogeneity by time period, season, fare policy, and shelter availability, with interaction evaluated using Wald tests. Cold thermal stress was associated with the largest, most persistent reductions in ridership, with cumulative declines of -15.7% (95% CI: -26.0%, -4.0%) over 0-24 h. Precipitation was associated with sharp but transient reductions concentrated within 3-6 h (-8.4% to -9.8%), with little evidence of longer-term displacement. Heat associations were weaker and context-dependent, with modest short-run increases, but net same-day declines. Associations varied by time of day and season, were attenuated during the free-fare period, and differed by shelter availability. In contrast, ozone exceedance and wildfire smoke exhibited limited and inconsistent associations, with measurable reductions primarily during morning commute during heavier smoke conditions. Overall, transit ridership is more strongly associated with short-term weather exposure, than with ambient air quality. Fare policy and stop-level infrastructure modify these associations, highlighting actionable strategies to enhance transit resilience under increasing climate variability.

The urban tree cover (UTC) is a key indicator for monitoring climate adaptation, guiding policy goals, and designing equitable cities. However, consistent UTC estimates remain limited. This study proposes a transferable UTC modelling framework using global open-source data to map fractional UTC. The method involves satellite spectral products and high-resolution canopy height data to build city-specific machine learning models of 19 Canadian cities. Model accuracy, assessed through cross-validation, ranged from R2 = 0.67-0.88 with RMSE values of 5.5-16.3%. Independent validation against airborne laser scanning in three cities demonstrated strong agreement (Pearson r = 0.82-0.92). Results suggest that UTC distributions are unevenly distributed, typically right skewed, with large parks and riparian corridors accounting for a disproportionate share of UTC. Greenness and moisture are the most influential predictors, while model residuals concentrate in areas with higher UTC. The resulting UTC maps and modelling framework provide a consistent and reproducible framework for UTC distribution assessments, benchmarking across diverse ecozones, and monitoring UTC change at urban scales over time.

Abstract The monarch butterfly (Danaus plexippus) is a species of iconic cultural interest. Thanks to annual overwintering monarch counts at hundreds of locations in coastal California, we are able to track fluctuations with high temporal and spatial resolution. Between 1997 and 2024, monarch populations at overwintering sites in the western United States experienced severe dips, at times (2018–2020, 2023–2024) giving the appearance of a population collapse. From 2018 to present, the Pismo State Beach Overwintering Monarch Grove has conducted multiple counts during overwintering and geolocated counts of individual monarch clusters to specific trees within the site. This study determined how annual monarch population variability is influenced by both climate and prior year population density at the state, region, and overwintering‐site scale. Furthermore, through a machine‐learning process, we investigated how overwintering site configuration and structure drive monarch winter space‐use dynamics within the Pismo Beach site. Our approach found monarchs exhibit a preference for specific overwintering sites in California, and that 64% of annual variability of counts across sites can be explained by climate and density dependence, with density dependence explaining 50% of total variability. Within the site we found very little regional climate effect, but individual trees, tree size, distance to boundary, and the amount of shade were all strong indicators of monarch presence. Additionally, only 11 out of 320 trees at the Pismo Beach site accounted for 83.6% of all counts over 6 years, highlighting how monarchs use specific trees and how tree structure may create preferred microclimates for clustering.

The scale and magnitude of urban heating are often assessed using Satellite-Derived Land Surface Temperature (SD-LST). Yet, discrepancies in spatial resolution limit SD-LST’s ability to reflect pedestrian thermal experience, potentially leading to ineffective mitigation strategies. Hyper-local measurements of urban heat, defined as surface temperatures (TS) at the scale of pedestrian activity (e.g., bus stops or street segments), may provide more accurate insights into thermal comfort. This study compares hyper-local ~0.01 m resolution TS collected via consumer-grade Forward-Looking Infrared (FLIR) thermography with resampled 30 m resolution SD-LST from Landsat 8 and 9 images to evaluate their utility in predicting thermal comfort indices across 60 bus stops in Denver, Colorado. During the summer of 2023, 270 FLIR measurements were collected over 19 dates, with a four-day subset (n = 33) coinciding with Landsat imagery. FLIR TS averaged 25.12 ± 5.39 °C, while SD-LST averaged 35.90 ± 12.56 °C, a significant 10.77 °C difference (95% CI: 6.81–14.73; p < 0.001). FLIR TS strongly correlated with biometeorological metrics such as air temperature and mean radiant temperature (r > 0.8; p < 0.001), while SD-LST correlations were weak (r < 0.3). Linear mixed-effects models using FLIR TS explained 50–66% of the variance in thermal comfort indices and met ISO 7726 standards. Each 1 °C increase in FLIR TS predicted a 0.75 °C rise in mean radiant temperature. These results highlight hyper-local thermography as a reliable, low-cost tool for urban heat resilience planning.

Because urban landscapes are heterogeneous, the methods and spatial resolution used to depict the land surface greatly influence the representation of urban features. Land cover products such as tree canopy cover (TCC) are particularly sensitive to the methodology and resolution used in their creation. Differences in TCC mapping have implications on the outcomes of ecosystem service (ES) models, including those underlying natural capital accounting. Here, we quantify the sensitivity of physical rainfall interception and local climate regulation ES models for 189 U.S. cities to TCC inputs from four TCC products: a) National Land Cover Database (NLCD), b) Enhanced NLCD TCC, c) aggregated city-specific composite, and d) global tree canopy height dataset. We find both city-level and aggregate differences in TCC estimates, from a 38% decrease to a 3% increase relative to an aggregated high-resolution product. These differences result in up to 3% overestimations and 27% underestimation of rainfall interception and 2–56% underestimation of local climate regulation ES. City size, population, and greenness in addition to climatic variation drove differences between TCC products, and this variation requires users to carefully consider the choice of input data for any planned analysis. Though high-resolution data can offer greater nuance and accuracy, more limited spatiotemporal availability can hinder their usefulness for long-term monitoring applications such as natural capital accounting. The differences found in this study provide valuable insights for making informed decisions on data inputs for use in urban ecosystem research and for contextualizing model outcomes.

Moderate-resolution (30-m) national map products have limited capacity to represent fine-scale, heterogeneous urban forms and processes, yet improvements from incorporating higher resolution predictor data remain rare. In this study, we applied random forest models to high-resolution land cover data for 71 U.S. urban areas, moderate-resolution National Land Cover Database (NLCD) Tree Canopy Cover (TCC), and additional explanatory climatic and structural data to develop an enhanced urban TCC dataset for U.S. urban areas. With a coefficient of determination (R2) of 0.747, our model estimated TCC within 3% for 62 urban areas and added 13.4% more city-level TCC on average, compared to the native NLCD TCC product. Cross validations indicated model stability suitable for building a national-scale TCC dataset (median R2 of 0.752, 0.675, and 0.743 for 1,000-fold cross validation, urban area leave-one-out cross validation, and cross validation by Census block group median year built, respectively). Additionally, our model code can be used to improve moderate-resolution TCC in other parts of the world where high-resolution land cover data have limited spatiotemporal availability.

Growing concerns about heat in urban areas paired with the sparsity of weather stations have resulted in individuals drawing on data from citizen science sensor networks to fill in data gaps. In the past decade, a proliferation of crowd-sourced sensors has provided low-cost local air quality and temperature, with one brand having over 14,000 sensors deployed in the United States between 1 January 2017 and 20 July 2021. Although the air quality data from PurpleAir sensors have been widely studied, less attention has been paid to reported temperature. Gridded modeled temperature datasets are widely used in epidemiologic studies. The spatial granularity of the crowd-sourced sensor data captures local temperature variation which existing gridded datasets cannot, and can potentially be used to generate exposure assessments for health research. We compare temperature metrics reported by the dominantly used crowd-sourced sensor in the United States with a gridded temperature product, the North American Land Data Assimilation System (NLDAS)-2, which although not a gold-standard measure of temperature, is widely used in epidemiologic research. We evaluate the lag between indoor and outdoor sensor temperatures. We report associations of the difference between outdoor sensor temperatures and NLDAS-2 temperatures, an indicator of degradation, and the duration of sensor operation. Finally, based on the temperature range recorded by the outdoor sensors vis-a-vis NLDAS-2 temperatures, we provide a list of 271 (2.5%) sensors potentially misclassified as outdoor and likely located indoors. We observed that the outdoor sensors agreed well with NLDAS-2 (R2 > 0.82). This association broke down under warm conditions (daily average NLDAS ≥21.1oC). Our comparison suggests that a radiative-correction needs to be applied to use crowd-sourced data reliably. However, the spatial granularity of the continental sensor network can reduce the measurement error in exposure assignment compared to the NLDAS-2. Indoor sensor temperatures lagged hourly NLDAS temperatures by 2 hours across almost all climate zones. The mean difference in hourly sensor and NLDAS-2 temperatures increased by 0.57oC for every operational year, suggesting that careful attention must be paid to degradation. Overall, we found that researchers should be aware of the limitations in crowd-sourced sensor air temperatures when examining extreme heat, or when aggregating sensor data across multiple years.

Montane meadows provide vital habitat that supports ecosystems, regulate hydrological processes, and offer valuable recreational opportunities. Meadows account for 3 % of Yosemite National Park's area, including Yosemite Valley, and are particularly susceptible to human impacts such as formation of informal trails. We collected observational data on visitor activity and quantified social trail disturbance to compare with resource monitoring datasets and through similar parameters to Walden-Schreiner and Leung (2013) who studied visitor use and behavior in three Yosemite Valley meadows in 2011. We documented change in number of visitors per hour and primary activity pursued. We also compared trends of meadow disturbance (informal trail length and disturbed area) from the early 2000s-2023. Informal trail length at El Capitan Meadow decreased from 2004 to 2018, followed by recent increases. Disturbed area for Cooks A, Sentinel A, Slaughterhouse B, Stoneman A, and Stoneman B sub-meadows increased from 2006 to 2023. Between 2011 and 2023, the proportion of those engaged in active versus stationary pursuits showed that visitors engaged in more active pursuits in two of three meadows. Moreover, there were >3.5 times more visitors per hour in Cooks, El Capitan, and Leidig meadows in 2023 compared to 2011, yet, most visitors utilized designated trails. Meadow locational context was linked to activity preference and types of impacts. Management strategies, such as fencing and signage have been effective at minimizing impacts in several meadows. Parsing nuances of informal trail use and drivers of disturbance for various meadows is crucial for informed park management decisions and actions.

Individuals are routinely exposed to traffic-related air pollution on their commutes, which has significant health impacts. Mitigating exposure to traffic-related pollution is a key urban sustainability concern. In Denver, Colorado, low-income Americans are more likely to rely on buses and spend time waiting at bus stops. Evaluating the contribution of traffic emissions at bus stops can provide important information on risks experienced by these populations. We measured PM2.5 constituents at eight bus stops and one background reference site in Denver, in the summer of 2023. Source profiles, including gasoline emissions from traffic, were estimated using Positive Matrix Factorization (PMF) analysis of PM2.5 constituents collected at a Chemical Speciation Network site in our study region. The contributions of the different sources at each bus stop were estimated by regressing the vector of species concentrations at each site (dependent variable) on the source-profile matrix from the PMF analysis (independent variables). Traffic-related emissions (~2.5–6.6 μg/m3) and secondary organics (~3–5 μg/m3) contributed to PM2.5 at the bus stops in our dataset. The highest traffic-related emissions-derived PM2.5 concentrations were observed at bus stops near local sources: a gas station and a car wash. The contribution of traffic-related emissions was lower at the background site (~1 μg/m3).

Home Owners' Loan Corporation (HOLC) maps illustrated patterns of segregation in United States cites in the 1930s. As the causes and drivers of demographic and land-use segregation vary over years, these maps provide an important spatial lens in determining how patterns of segregation spatially and temporally developed during the past century. Using a high-resolution land-use time series (1937-2018) of Denver, Colorado, USA, in conjunction with 80 years of U.S. Census data, we found divergent land-use and demographics patterns across HOLC categories were both pre-existent to the establishment of HOLC mapping and continued to develop over time. Over this period, areas deemed "declining" or "hazardous" had more diverse land use compared to "desirable" areas. "Desirable" areas were dominated by one land-use type (single-family residential), while single-family residential diminished in prominence in the "declining/hazardous" areas. This divergence became more established decades after HOLC mapping, with impact to racial metrics and low-income households. We found changes in these demographic patterns also occurred between 2000 and 2019, highlighting how processes like gentrification can develop from both rapid demographic and land-use changes. This study demonstrates how the legacy of urban segregation develops over decades and can simultaneously persist in some neighborhoods while providing openings for fast-paced gentrification in others.

The anthropogenic urban realm exacerbates surface urban heat island (UHI) effects, triggering health hazards, such as mortality attributable to heat exposure in cities. The study makes a concerted effort to unravel the complex interplay between various spatial, quantitative, and qualitative attributes of vegetation, aiming to comprehend its pivotal role in mitigating urban heat risks within urban environments. The UHI risk is related to land surface temperature (LST). The study models UHI risk in twelve American cities in diverse Köppen-Geiger Climate zones spanning the contiguous USA. To address this, the Integrated Modelling approach by the ARtificial Intelligence for Environment & Sustainability (ARIES) initiative has been adopted in the study. This approach based on FAIR (Findable, Accessible, Interoperable, and Reusable) principles is accessible at https://aries.integratedmodelling.org/. Utilizing the k.LAB software with semantic reasoning our modeling approach assesses the UHI risk. It maps the spatial distribution of UHI considering hotspots of anthropogenic heat, vegetation, land cover, and land surface temperature. UHI risk is assessed at a resolution of 30 meters alongside census tract-level data using an ordered weighted approach. The study found variations in the relationship between greenness, as indicated by the Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST) across 12 different cities. The findings highlight the cooling effect of the water bodies, especially in areas near the port and green spaces. Linear parks such as roadside tree plantations typically feature uniform tree species and often lack smaller trees and shrubs, making them susceptible to heat infiltration from surrounding areas and resulting in a lesser overall temperature reduction. It identifies at least 30 percent of census tracts across 12 cities necessitate urban greening intervention. The study provides scientific insight into the cooling effects of urban parks, offering valuable guidance for urban planning and aiding decision-makers in addressing the UHI effect and enhancing overall urban sustainability. The study also underscores the significance of open science in developing environmental models addressing global sustainability challenges concerning the pressing issue of assessing urban climate risks. Models and scientific artifacts often face challenges in reusability, transferability, and sharing across diverse programming languages or modeling systems, revealing a significant lack of interoperability. By delving into the factors of LAI, NDVI, and Landscape Shape Index (LSI), the study aims to enhance understanding of the role of vegetation in ameliorating the adverse effects associated with UHI, thus paving the way for more effective urban heat management strategies.

Urban land cover types influence the urban microclimates. However, recent work indicates the magnitude of land cover's microclimate influence is affected by aridity. Moreover, this variation in cooling and warming potentials of urban land cover types can substantially alter the exposure of urban areas to extreme heat. Our goal is to understand both the relative influences of urban land cover on local air temperature, as well as how these influences vary during periods of extreme heat. To do so we apply predictive machine learning models to an extensive in-situ microclimate and 1 m land cover dataset across eight U.S. cities spanning a wide aridity gradient during typical and extreme heat conditions. We demonstrate how the cooling influence of tree canopy and the warming influence of buildings on microclimate linearly scales with regional aridity, while the influence of turf and impervious surfaces does not. These interactions lead tree canopy to consistently mitigate to air temperature increases during periods extreme heat in arid cities, while the influence of urban tree canopy on extreme heat in humid regions is varied, suggesting that mitigation is possible, but tree canopy can also aggravate extreme heat or have no significant effect.

The decline of the iconic monarch butterfly ( Danaus plexippus ) in North America has motivated research on the impacts of land use and land cover (LULC) change and climate variability on monarch habitat and population dynamics. We investigated spring and fall trends in LULC, milkweed and nectar resources over a 20-year period, and ~ 30 years of climate variables in Mexico and Texas, U.S. This region supports spring breeding, and spring and fall migration during the annual life cycle of the monarch. We estimated a − 2.9% decline in milkweed in Texas, but little to no change in Mexico. Fall and spring nectar resources declined < 1% in both study extents. Vegetation greenness increased in the fall and spring in Mexico while the other climate variables did not change in both Mexico and Texas. Monarch habitat in Mexico and Texas appears relatively more intact than in the midwestern, agricultural landscapes of the U.S. Given the relatively modest observed changes in nectar and milkweed, the relatively stable climate conditions, and increased vegetation greenness in Mexico, it seems unlikely that habitat loss (quantity or quality) in Mexico and Texas has caused large declines in population size or survival during migration.

Despite being long-lived and massive, giant sequoias (Sequoiadendron giganteum (Lindl.) J. Bucholz) are susceptible to erosion given their relatively shallow root structure. Human-caused soil compaction and vegetation loss through social trails are primary drivers of erosion in giant sequoia groves, particularly for trees that are near formal trails and access roads. We develop a method to observe and quantify the near-tree impacts from park visitors and to relate the overall amount of use with ground cover impact parameters to assess whether the desired conditions of each grove are being met for the park to maintain a spectrum of recreational opportunities. We collected data on visitation, ground cover, soil compaction, and social trailing using a combination of targeted surveys and observations at the three giant sequoia groves in Yosemite National Park. The Mariposa Grove receives the most visitation, and use levels among groves were consistent with relative size and facilities available. Selected parameters for ground cover data were analyzed by comparing values within undisturbed versus trampling-disturbed subplots at both 0–2 m and 2–8 m. Exposed soil cover and compaction were generally higher in anthropogenically disturbed subplots versus undisturbed subplots, and vegetation cover was reduced in some disturbed subplots. Each grove had one surveyed tree where average soil compaction was ≥2.2 kg/cm2, which may limit root growth and impact seedling regeneration. Each of the three groves had some trees with social trail presence, yet less than 7% of mature trees within any grove were impacted by social trails, and most social trails were rated as having low impairment. Coupling soil compaction measurements and estimates of trampling-disturbed areas with mapping of social trail conditions within groves provides a general assessment of visitor-associated impacts to sequoia groves and can facilitate a relatively rapid way to track hotspot (i.e., increasingly impacted) trees over time.

Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks. Significance Statement Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.

Urbanization creates novel ecosystems comprised of species assemblages and environments with no natural analogue. Moreover, irrigation can alter plant function compared to non-irrigated systems. However, the capacity of irrigation to alter functional trait patterns across multiple species is unknown but may be important for the dynamics of urban ecosystems. We evaluated the hypothesis that urban irrigation influences plasticity in functional traits by measuring carbon-gain and water-use traits of 30 tree species planted in Southern California, USA spanning a coastal-to-desert gradient. Tree species respond to irrigation through increasing the carbon-gain trait relationship of leaf nitrogen per specific leaf area compared to their native habitat. Moreover, most species shift to a water-use strategy of greater water loss through stomata when planted in irrigated desert-like environments compared to coastal environments, implying that irrigated species capitalize on increased water availability to cool their leaves in extreme heat and high evaporative demand conditions. Therefore, irrigated urban environments increase the plasticity of trait responses compared to native ecosystems, allowing for novel response to climatic variation. Our results indicate that trees grown in water-resource-rich urban ecosystems can alter their functional traits plasticity beyond those measured in native ecosystems, which can lead to plant trait dynamics with no natural analogue.

Semi-arid urban environments are undergoing an increase in both average air temperatures and in the frequency and intensity of extreme heat events. Within cities, different composition and densities of urban landcovers (ULC) influence local air temperatures, either mitigating or increasing heat. Currently, understanding how combinations of ULC influence air temperature at the block to neighborhood scale is necessary for heat mitigation plans, and yet limited due to the complexities integrating high-resolution ULC with spatial and temporally high-resolution microclimate data. We quantify how ULC influences air temperature at 60 m resolution for day and nighttime climate normals and extreme heat conditions by integrating microclimate sensor data sensor and high-resolution (1 m2) ULC for Denver, Colorado's urban core. We derive ULC drivers of air temperature using a structural equation model, then use a random forest algorithm to predict air temperatures for 30-year climate normals and an extreme heat condition. We find that, in conjunction with other ULC, urban tree canopy reduces daytime air temperatures (-0.026 °C per % cover), and the combination of impervious surfaces and buildings increases daytime air temperature (0.021 °C per % cover). Compared to daytime hours, nighttime irrigated turf temperature cooling effects are increased from being non-significant to -0.022 °C per % cover, while tree canopy effects are reduced from -0.026 °C during the day to -0.016 °C at night. Overall, ULC drives ~17% and 25% of local air temperature during the day and night, respectively. ULC influence on daytime air temperatures is altered in extreme heat events, both depending on the ULC type and time of day. Our findings inform urban planners seeking to identify potential hot and cool spots within a semi-arid city and mitigate high urban air temperatures through using ULC within larger urban climate mitigation strategies.

High nighttime urban air temperatures increase health risks and economic vulnerability of people globally. While recent studies have highlighted nighttime heat mitigation effects of urban vegetation, the magnitude and variability of vegetation-derived urban nighttime cooling differs greatly among cities. We hypothesize that urban vegetation-derived nighttime air cooling is driven by vegetation density whose effect is regulated by aridity through increasing transpiration. We test this hypothesis by deploying microclimate sensors across eight United States cities and investigating relationships of nighttime air temperature and urban vegetation throughout a summer season. Urban vegetation decreased nighttime air temperature in all cities. Vegetation cooling magnitudes increased as a function of aridity, resulting in the lowest cooling magnitude of 1.4 °C in the most humid city, Miami, FL, and 5.6 °C in the most arid city, Las Vegas, NV. Consistent with the differences among cities, the cooling effect increased during heat waves in all cities. For cities that experience a summer monsoon, Phoenix and Tucson, AZ, the cooling magnitude was larger during the more arid pre-monsoon season than during the more humid monsoon period. Our results place the large differences among previous measurements of vegetation nighttime urban cooling into a coherent physiological framework dependent on plant transpiration. This work informs urban heat risk planning by providing a framework for using urban vegetation as an environmental justice tool and can help identify where and when urban vegetation has the largest effect on mitigating nighttime temperatures.