Anthropogenic Heat Flux Research Articles

Understanding the Role of Sea Surface Temperature and Urbanization on Severe Thunderstorms Dynamics: A Case Study in Surabaya, Indonesia

AbstractWithin the period 2014–2017, five hail events were reported in the city of Surabaya in Indonesia. Although deep convection commonly develops over the Maritime Continent, severe thunderstorms triggering hail events develop less frequently as specific atmospheric conditions are required. The rapid urbanization in Surabaya might have led to increased heat release to the atmosphere and to the deepening of convection, which raises the question of whether urbanization is the culprit of the recent hail events in Surabaya. Hence, for a selected hail event, we used the high‐resolution Weather Research and Forecasting model to understand the storm dynamics and to explore the role of urbanization, sea surface temperature, and aerosol concentration on the storm dynamics with a total of 11 scenarios. The control simulation reveals that low‐level convergence induced by a sea breeze creates instability. At the same time, the urban heat release enhances the energy supply to induce hail formation and retain the storm's lifetime over the city. A factor separation method revealed that the urbanization (added anthropogenic heat flux, urban aerosol, and the rise in building height) and the sea surface temperature increase contribute to the storm enhancement over Surabaya, producing two times higher updraft velocity, doubling the maximum graupel mass mixing ratio and finally resulting in 15%–30% higher accumulated precipitation over Surabaya, compared to the control simulation.

Deciphering the sensitivity of urban canopy air temperature to anthropogenic heat flux with a forcing-feedback framework

The sensitivity of urban canopy air temperature () to anthropogenic heat flux () is known to vary with space and time, but the key factors controlling such spatiotemporal variabilities remain elusive. To quantify the contributions of different physical processes to the magnitude and variability of (where represents a change), we develop a forcing-feedback framework based on the energy budget of air within the urban canopy layer and apply it to diagnosing simulated by the Community Land Model Urban over the contiguous United States (CONUS). In summer, the median is around 0.01 over the CONUS. Besides the direct effect of on , there are important feedbacks through changes in the surface temperature, the atmosphere–canopy air heat conductance (), and the surface–canopy air heat conductance. The positive and negative feedbacks nearly cancel each other out and is mostly controlled by the direct effect in summer. In winter, becomes stronger, with the median value increased by about 20% due to weakened negative feedback associated with . The spatial and temporal (both seasonal and diurnal) variability of as well as the nonlinear response of to are strongly related to the variability of , highlighting the importance of correctly parameterizing convective heat transfer in urban canopy models.

Effects of Urban Expansion and Anthropogenic Heat Enhancement on Tropical Cyclone Precipitation in the Greater Bay Area of China

AbstractThe impact of urban expansion and anthropogenic heat (AH) enhancement on tropical cyclone precipitation (TCP) in the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is investigated by using the Weather Research and Forecasting (WRF) model. Sensitivity experiments are conducted during the landfall periods of two tropical cyclones, Hato (2017) and Mangkhut (2018), by artificially varying the surface AH flux from 0 to 600 W/m2 and the urban land surface from 1985 to 2017, respectively. Results show that the TCP in the GBA's urban region increases with both urban expansion and AH enhancement. However, in the urban downstream region, only AH enhancement causes an increase in TCP, whereas urban expansion has no significant influence on TCP. AH enhancement and urban expansion affect surface temperature, surface sensible heat flux, and surface latent heat flux, leading to changes in atmospheric instability and surface water evaporation, resulting in changes in low‐level moisture convergence. The change patterns of the vertically integrated moisture flux divergence between 850 and 910 hPa in the urban and urban downstream regions of the GBA are consistent with those of the TCP changes in this area, indicating that low‐level moisture convergence is the key factor determining the TCP changes in the region.

Revealing the response of urban heat island effect to water body evaporation from main urban and suburb areas

The urban heat island (UHI) effect is accelerated with urbanization and climate change, thus threatening human survival. The evaporation from water body (E) takes away energy through heat absorption process, thereby effectively play a role in temperature cooling and UHI effect alleviation. However, the response of UHI effect to urban E is still lacks study in the current UHI research. To address this issue, this work proposes a customized water body evaporation model in urban areas. The newly developed urban E model considers the contribution of anthropogenic heat flux (AHF) to the energy balance in urban areas. Meanwhile, AHF is also used to enhance the simulation of the water heat storage change (G) for urban water body. Validation results in two megacities in China indicate that the developed urban E model which considered AHF in the energy balance equation significantly improves the simulation performance of E in the main urban area (the root mean square error (RMSE) significantly decreased by 26.6 W/m2 compared with the original Penman formula for E in the main urban area (Eu) simulation). The consideration of AHF in the G determination improves the simulation performance of E in the deep water body (the RMSE significantly decreased by 33.3 W/m2 compared to the AHF-Penman model that do not considering G for E simulation in deep water body). The developed urban E model is further used to evaluate the response of UHI to the Eu and E in the suburban area (Es). It is found that Eu effectively alleviates UHI, while Es aggravate UHI. Moreover, the cooling effect of E in the main urban areas (ΔTau) and suburbs (ΔTas) are increased with urbanization. The increasing rate of ΔTau is higher than ΔTas, indicate the contribution of evaporation cooling to the UHI alleviation is increased with urbanization. Further analysis demonstrate the urbanization process can explain approximately 90% of the enhanced ability of E to mitigate the UHI effect. Correlation analysis shows that the mitigation capability of E to UHI effect is mainly controlled by the volume and surface size of water body. Finally, future climate scenario-based urban E forecast confirms that ΔTau and ΔTas will continue to rise with climate change. The average increasing rate are 0.018 °C/year and 0.013 °C/year for ΔTau and ΔTas, respectively, under the three representative concentration pathways. The increasing rate of ΔTau is larger than ΔTas, suggesting the mitigation of the UHI effect will benefit more from urban E under the future climate change. Generally, our findings highlight that the mitigation of the UHI effect mainly benefits from E in the main urban area rather than E in the suburban area. This study gains insight into E in urban areas, including its algorithm, interaction with the UHI effect, and responses to urbanization and climate change. The results of this study provide a good scientific basis for urban landscape water planning.

Assessing the performance of the non-hydrostatic RegCM4 with the improved urban parameterization over southeastern China

This study evaluates the performance of the non-hydrostatic RegCM4 model (RegCM4-NH) incorporating the building energy model (BEM) in simulating urban climate over the Pearl River Delta (PRD) and Yangtze River Delta (YRD) regions. Specifically, it focuses on examining how the inclusion of BEM can ameliorate the severe warm bias in urban grids observed in high-resolution RegCM4 simulations by improving the surface energy balance. The prognostic calculation of interior building temperature and the consideration of ventilation and heat transfer between the building and the environment can collectively lead to a better estimate of anthropogenic heat flux (AHF). The effect of BEM does not appear to be uniform, showing spatial and temporal variations. While the magnitude of AHF reduction is roughly proportional to the urban density, the maximum reduction occurs at nighttime, which results in an asymmetric response to the minimum and maximum temperatures. Although the degree of lowering temperature is not sufficient to remove the systematic warm bias, adding physical realism to the model is helpful to make further improvements. The performance evaluation of RegCM4-NH with BEM targeted in vast urban agglomerations for the first time will be a valuable reference to facilitate the wider use of RegCM4-NH in the study of urban surface impacts.

Luojia nighttime light data with a 130m spatial resolution providing a better measurement of gridded anthropogenic heat flux than VIIRS

Anthropogenic heat flux (AHF) was generally estimated using the nighttime light (NTL) data with a 500 m (NPP-VIIRS) spatial resolution, creating significant uncertainties and distortions. A few studies have attempted to estimate AHF using a 130 m NTL data from the newly launched Luojia 1–01 satellite. However, there is a general lack of work aimed at comparing and validating the advantages of Luojia over its predecessor NTL data on AHF estimation. Therefore, we estimated AHF using Luojia and VIIRS and analyzed the consistency and accuracy of these results by using land cover/land use data. The analysis reveals that VIIRS overestimates the AHF for the natural lands and underestimates the AHF for the urban lands. Compared with VIIRS, Luojia can reduce 1.5–46.9% of AHF overestimations for the natural lands and 13.1–214.9% of AHF underestimations for the urban lands. The analysis also demonstrates that vegetation adjustment is unnecessary to AHF estimation using Luojia NTL data. Besides, the radiance value of Luojia NTL data is more suitable for AHF estimation than other forms (e.g., the digital number value, logarithmic transformation of the radiance value). These results can provide valuable information to improve the understanding of anthropogenic heat and urban thermal environments.

Urban anthropogenic heat index derived from satellite data

The increasing anthropogenic heat, which is emitted from human activities and energy consumption in urban areas and migrates to the atmosphere, significantly alters surface energy balances and the local climate and in turn generates urban heat islands (UHIs). Anthropogenic heat flux (AHF) has been estimated with energy balance residual and inventory methods. However, these methods have large uncertainty and fail to further explore the spatial distribution of anthropogenic heat at large scales due to limited data availability and the complexities of human activities, urban geometry, landscapes and building thermal properties. From the heat sources of the UHIs and based on thermodynamics, this study deduces that the AHE difference between urban and surrounding rural areas can be quantified as the residual of the heat for increasing UHIs and the heat difference from natural radiation between urban and rural areas. Thus, an urban anthropogenic heat index (UAHI) is proposed to quantify the contribution of AHF to UHIs, as a proxy of AHF. The UAHI is generated using the normalized differences in urban–rural land surface temperature (LST) and albedo derived from both Landsat and MODIS data. We investigate the spatial and temporal association between the UAHI and AHF in Beijing. The results demonstrated that the average annual UAHIs in Beijing ranged from 0.168 to 0.208 and steadily improved in the last decade. The UAHI can represent detailed spatial heterogeneity and temporal variations in AHF with correlation coefficients in the range from 35% to 50%. UAHI can also identify the dynamics of AHF hotspots and the heating and cooling sources in Beijing. Overall, the UAHI is a convincing indicator of the contribution of urban anthropogenic heat to UHIs, providing a new option for quantifying AHF dynamics and improving the knowledge of urban thermal environments.

Integration of flux footprint and physical mechanism into convolutional neural network model for enhanced simulation of urban evapotranspiration

Estimating urban evapotranspiration (ET) is of great significance for urban water resource allocation and assessing the urban heat island effect. However, most current urban ET models are based on the energy balance theory to estimate urban ET. These models lead to significant errors in urban ET simulation due to the surface heterogeneity and the existence of anthropogenic heat fluxes in urban areas. To solve this issue, this study proposes a modified machine learning-based urban ET method that can estimate urban ET at the site and regional scales. To better characterize the heterogeneity of urban surfaces, the flux footprint of in-situ ET and physical mechanism of ET process are integrated into the convolutional neural network (CNN) model. The modified CNN model is tested in a fast-developing city: Shenzhen, China based on two Eddy Correlation (EC) observations. The verification results indicated that coupling flux footprint and physical mechanism into the CNN model could effectively improve the accuracy of urban ET simulation at the site scale. The modified CNN model significantly reduced the root-mean-square-error (RMSE) of 25.8 W/m2 and increased the determination coefficient (R2) of 0.17 compared to the CNN-O model (The CNN-O model is defined as the CNN model do not integrate flux footprint and physical mechanism of ET). Further analyses suggested that fusing flux footprint data into a machine learning model helps enhance ET estimation in regions with high heterogeneity and highly variable wind directions. Moreover, the integration of physical mechanisms significantly enhanced the model capability to simulate extreme ET events. The modified CNN model is further applied to map the spatial distribution of urban ET and reconstruct long-term urban ET changes. The spatial pattern of urban ET exhibited large spatial variability, where the urban ET in water bodies (mean λET larger than 480 W/m2) and vegetation-covered areas (mean λET larger than 260 W/m2) are substantially higher than the impervious surfaces (mean λET less than 30 W/m2). Long-term trend analyses demonstrated that urbanization resulted in decline in urban ET. The average decreasing rate of urban ET is 1.61 mm/yr (P < 0.05), with a 18 % decrease relative to the long-term ET average. The leading causes for the decline of urban ET are the increased impervious surfaces and the decreased radiation. This study improved the simulation accuracy of urban ET and revealed the response of urban ET to urbanization.

The impacts of urban anthropogenic heat and surface albedo change on boundary layer meteorology and air pollutants in the Beijing-Tianjin-Hebei region

In this study, present and future anthropogenic heat flux (AH) and surface albedo (SA) in Beijing are estimated and incorporated into the WRF-Chem/UCM model. A series of nested model simulations are conducted at 9 km and 27 km horizontal grid resolution with 30 layers in the vertical to explore the sensitivity of model results to urban surface characteristics during the study period from 23 February to 13 March 2014, and the impacts of anthropogenic heat and surface albedo changes in the future on urban meteorology and air pollutants in Beijing. Model validation demonstrates that the model with the up-to-date urban surface parameters apparently improves predictions for both meteorological variables and chemical species compared with model results with default urban parameters. The combined effect of SA increase and AH decrease leads to consistent decreases in T2 (air temperature at 2 m above ground), WS10 (wind speed at 10 m) and PBLH (planetary boundary layer height) by up to 1.2 °C, 0.4 m s−1 and 200 m (33%), respectively, in Beijing averaged over the study period. In response to the meteorological changes, near surface PM2.5 (particulate matter with aerodynamic equivalent diameter below 2.5 μm) concentration increases by up to 15.6 μg m−3 (8.6%), whereas O3 concentration decreases by 2.6 ppb (25.1%), respectively. In daytime, near surface cooling due to SA increase and AH decrease causes a strong downdraft over Beijing urban areas, a wind divergence near the surface and an upslope wind over the western mountain, resulting in aerosol transport from urban district to western mountain slope. At nighttime, surface cooling is weaker than that in daytime, leading to a weaker downdraft over Beijing and a slight downslope wind in the mountain. The combined changes in SA and AH lead to a decrease in T2 by 1.2 °C, a decrease of O3 concentration by approximately 20%, and increases in PM2.5 and its components by 10–17% in daytime averaged over the urban areas of Beijing during the study period, and the above changes are weaker during haze days than those in clean days. The changes in urban surface parameters in Beijing in the future may be benefit for mitigating heat wave and O3 pollution risks, but could increase fine aerosol level and human health risk, which require an optimal strategy for co-benefits of climate change mitigation and air pollution control in future city planning.

Exploring the effect of COVID-19 pandemic lockdowns on urban cooling: A tale of three cities

COVID-19 pandemic has had a major impact on our society, environment and public health, in both positive and negative ways. The main aim of this study is to monitor the effect of COVID-19 pandemic lockdowns on urban cooling. To do so, satellite images of Landsat 8 for Milan and Rome in Italy, and Wuhan in China were used to look at pre-lockdown and during the lockdown. First, the surface biophysical characteristics for the pre-lockdown and within-lockdown dates of COVID-19 were calculated. Then, the land surface temperature (LST) retrieved from Landsat thermal data was normalized based on cold pixels LST and statistical parameters of normalized LST (NLST) were calculated. Thereafter, the correlation coefficient (r) between the NLST and index-based built-up index (IBI) was estimated. Finally, the surface urban heat island intensity (SUHII) of different cities on the lockdown and pre-lockdown periods was compared with each other. The mean NLST of built-up lands in Milan (from 7.71 °C to 2.32 °C), Rome (from 5.05 °C to 3.54 °C) and Wuhan (from 3.57 °C to 1.77 °C) decreased during the lockdown dates compared to pre-lockdown dates. The r (absolute value) between NLST and IBI for Milan, Rome and Wuhan decreased from 0.43, 0.41 and 0.16 in the pre-lockdown dates to 0.25, 0.24, and 0.12 during lockdown dates respectively, which shows a large decrease for all cities. Analysis of SUHI for these cities showed that SUHII during the lockdown dates compared to pre-lockdown dates decreased by 0.89 °C, 1.78 °C, and 1.07 °C respectively. The results indicated a high and substantial impact of anthropogenic activities and anthropogenic heat flux (AHF) on the SUHI due to the substantial reduction of huge anthropogenic pressure in cities. Our conclusions draw attention to the contribution of COVID-19 lockdowns (reducing the anthropogenic activities) to creating cooler cities.

Strong Modulation of Human‐Activity‐Induced Weekend Effect in Urban Heat Island by Surface Morphology and Weather Conditions

AbstractThe weekend effect of the canopy urban heat island (UHI) has been long recognized. However, how the UHI weekend effect (UWE) varies with the hour of day and season of year is still unclear; it remains largely unknown on how the UWE is regulated by various controls. To address these knowledge gaps, here we took Beijing, China as an example and investigated the detailed spatiotemporal UWE patterns and the major regulators with a 3‐year data set of in‐situ surface air temperatures. Our results indicate that the annual ΔIc (the UHI intensity difference on weekends and weekdays) is stronger at night (−0.13 ± 0.12 K; mean ± 1 STD) than during the day (−0.05 ± 0.10 K); at the seasonal scale, ΔIc reaches the strongest in winter (−0.14 K) and the weakest in summer (−0.05 K). The ΔIc is strongly regulated by anthropogenic heat flux (AHF), evidenced by a quasi‐synchronous diurnal pattern between ΔIc and ΔAHF (i.e., the AHF difference between weekends and weekdays). The nighttime ΔIc is intensely modulated by urban morphology, with a stronger modulation by the landscape shape index than by the distance of the station from the urban center. Weather conditions also modulate the ΔIc, with the ΔIc weakening with the increase of cloud coverage and wind speed level. We consider these findings deepen our understanding of the weekly rhythms of UHI as well as the underlying modulators.

Global mapping of surface 500-m anthropogenic heat flux supported by multi-source data

Anthropogenic heat emission affects the surface energy budget, energy exchange, and urban heat islands. Accurate quantification of anthropogenic heat flux (AHF) is significant for understanding the urban climate and improving human settlements. However, accurate global AHF dataset with high spatial heterogeneity (e.g., hundreds of meters) is still limited in practical application. In this study, we integrated multi-source data (global energy consumption statistics, nighttime light data, LandScan population density data, and regional AHF data) to model the global surface 500 m × 500 m AHF in 2016. The evaluation results indicate that the R2 between the global AHF integrated with regional fine data (GAHF) and the national energy consumption is 0.77, which is higher than the R2 (0.70) between the initial modeled AHF (GAHForg) and the national energy consumption. Besides, the mean R2 of GAHF and comparison dataset (0.84) is slightly higher than that of GAHForg (0.83) on the regional scale. On the grid scale, the correlation coefficient of GAHF and comparison dataset is improved compared with that of GAHForg. The absolute difference between GAHForg and China's surface AHF (CAHF) is 9.36 W·m−2, and that of between GAHF and CAHF is 0.97 W·m−2. These results demonstrate the feasibility of the global AHF estimation scheme, and the reliability of the global AHF estimates. The results can serve the global environment and climate change research.

Urban evapotranspiration estimation based on anthropogenic activities and modified Penman-Monteith model

Urban evapotranspiration (ET) is an essential part of the hydrological cycle. A complete understanding of urban ET is necessary to understand the interaction between the atmosphere and the urban surface. However, estimating urban ET is challenging due to the influence of urban surface heterogeneity and human activities in urban areas. Therefore, this paper proposed an improved multisource parallel model (PM-urban) based on the Penman-Monteith model, which is suitable for the ET estimation for the underlying surface of the city. The PM-urban model takes into account the violent human activities in the city, and its main characteristics are: (1) the anthropogenic heat flux (AHF) was included in the surface energy balance formula; (2) The building water dissipation was considered. In this paper, taking Shenzhen, one of the cities with the fastest urbanization process globally, as an example, seven urban ET maps of Shenzhen were generated using Landsat 8 images. The model was verified using pan evaporation data corrected by complementary relationships. The results showed that: (1) The ET of each land use type has the following relationship: water body > vegetation > bare soil > no building impervious surface, and the value of building water dissipation varies due to the height and type of buildings significant; (2) The PM-urban model that considers AHF is better than the original model that ignores AHF; (3) This model can describe the characteristics of urban ET well and explain the phenomenon of high water consumption in cities. Our research results provide a new perspective for calculating the urban ET, which can be applied to urban management, planning, and reducing the urban heat island effect.

Revising the definition of anthropogenic heat flux from buildings: role of human activities and building storage heat flux

Abstract. Buildings are a major source of anthropogenic heat emissions, impacting energy use and human health in cities. The difference in magnitude and time lag between building energy consumption and building anthropogenic heat emission is poorly quantified. Energy consumption (QEC) is a widely used proxy for the anthropogenic heat flux from buildings (QF,B). Here we revisit the latter's definition. If QF,B is the heat emission to the outdoor environment from human activities within buildings, we can derive it from the changes in energy balance fluxes between occupied and unoccupied buildings. Our derivation shows that the difference between QEC and QF,B is attributable to a change in the storage heat flux induced by human activities (ΔSo-uo) (i.e. QF,B=QEC-ΔSo-uo). Using building energy simulations (EnergyPlus) we calculate the energy balance fluxes for a simplified isolated building (obtaining QF,B, QEC, ΔSo-uo) with different occupancy states. The non-negligible differences in diurnal patterns between QF,B and QEC are caused by thermal storage (e.g. hourly QF,B to QEC ratios vary between −2.72 and 5.13 within a year in Beijing, China). Negative QF,B can occur as human activities can reduce heat emission from a building but this is associated with a large storage heat flux. Building operations (e.g. opening windows, use of space heating and cooling system) modify the QF,B by affecting not only QEC but also the ΔSo-uo diurnal profile. Air temperature and solar radiation are critical meteorological factors explaining day-to-day variability of QF,B. Our new approach could be used to provide data for future parameterisations of both anthropogenic heat flux and storage heat fluxes from buildings. It is evident that storage heat fluxes in cities could also be impacted by occupant behaviour.

Estimation of anthropogenic heat release in Mexico City

The urban heat island (UHI) is a phenomenon that appears in cities due to the alteration of the energy balance caused by the drastic land use change. A factor that can contribute to its development is the energy generated by the city inhabitants, or the anthropogenic heat flux (QF). Therefore, the determination of this component can improve the predictability of UHI development and variability. The objective of this work was to estimate the main sources of anthropogenic heat flux, based on a vehicle classification (taxis, cars, minibuses and buses), surveys of electricity consumption and population density in Mexico City. The anthropogenic heat determination is based on a simple distribution of heat generated by vehicles, buildings and people. Vehicle classification was made through video analysis and the vehicle flow was also accounted for with sensors at the road level, within a radius of 1 km. The consumer surveys were applied to businesses and homes, and with the support of basic geostatistical areas the population density was determined. The analysis showed that the highest heat emission occurs from 15:00 to 21:00 h. This heat flux in average was not significant when compared to net radiation. However, anthropogenic heat flux generation in dense areas of Mexico City, can exceed 75 W·m−2 for certain times of the day, while the inventory carried out QF averaged 24.7 W·m−2 at San Agustin, in the historical center. With the obtained results, also a standard car was idealized, and the electricity consumption profiles of some sectors were generated, which will allow obtaining the heat flux more easily and quickly. According to the results, it is advisable to integrate the anthropogenic heat flux component into the energy balance for future studies of the UHI.

About the Annual Course of Moscow Heat Island and the Impact on It of the Quarantine Measures to Prevent the COVID-19 Pandemic in 2020

Seasonal differences in the Moscow urban heat-island intensity (UHII) have been studied in detail based on data obtained in 2018–2020 by the meteorological network of stations located in Moscow and Moscow region. It is shown that the annual cycle of this phenomenon is slightly pronounced. In most cases, the UHI is manifested stronger in summer and weaker in winter; however, in some months, the situation may be reverse. The question of the statistical significance of seasonal differences remains open. The closest statistical relationship was revealed between the UHI and lower clouds during the night hours, so that its highest intensity is observed in the least cloudy seasons (usually in summer). The UHII distribution functions are close to the normal law in summer and spring, and, in winter and fall, they are characterized by a noticeable positive asymmetry, because their values decrease and the mode approaches the lower physical limit. The period of strict quarantine restrictions during the COVID-19 pandemic in the spring and early summer of 2020 led to a rapid and statistically significant decrease in the Moscow UHII, probably due to both natural factors (increased cloudiness) and human activities (rapidly decreased anthropogenic heat fluxes and a weakened urban industrial haze creating an additional counterradiation).

Improving the WRF/urban modeling system in China by developing a national urban dataset

Accurate modeling of urban climate is essential to predict potential environmental risks in cities. Urban datasets, such as urban land use and urban canopy parameters (UCPs), are key input data for urban climate models and largely affect their performance. However, access to reliable urban datasets is a challenge, especially in fast urbanizing countries. In this study, we developed a high-resolution national urban dataset in China (NUDC) for the WRF/urban modeling system and evaluated its effect on urban climate modeling. Specifically, an optimization method based on building morphology was proposed to classify urban land use types. The key UCPs, including building height and width, street width, surface imperviousness, and anthropogenic heat flux, were calculated for both single-layer Urban Canopy Model (UCM) and multiple-layer Building Energy Parameterization(BEP). The results show that the derived morphological-based urban land use classification could better reflect the urban characteristics, compared to the socioeconomic-function-based classification. The UCPs varied largely in spatial within and across the cities. The integration of the developed urban land use and UCPs datasets significantly improved the representation of urban canopy characteristics, contributing to a more accurate modeling of near-surface air temperature, humidity, and wind in urban areas. The UCM performed better in the modeling of air temperature and humidity, while the BEP performed better in the modeling of wind speed. The newly developed NUDC can advance the study of urban climate and improve the prediction of potential urban environmental risks in China.

Spatial pattern of anthropogenic heat flux in monocentric and polycentric cities: The case of Chengdu and Chongqing

• Anthropogenic heat flux (AHF) had spatial variations under distinct urban forms. • Building and traffic contributed more AHF than industry and human metabolism. • AHF pattern was contiguous in Chengdu and clustered in Chongqing. • High AHF gathered in Chengdu's center but dispersed in Chongqing's several centers. Although anthropogenic heat emission (AHE) has been acknowledged as a significant threat to urban thermal environment, little attention has been paid to the differences in the intensity of AHE, namely anthropogenic heat flux (AHF), under distinct urban forms. Therefore, we chose one monocentric city (Chengdu) and one polycentric city (Chongqing) in China to compare the spatial pattern of AHF. AHE was calculated from four aspects of buildings, traffic, industries, and human metabolism for the whole cities and then allocated to the blocks using the inventory-based method. The results showed that building and traffic heat accounted for major AHE. In Chengdu, annual mean AHF decreased from the urban core to the suburbs, forming a circular and contiguous pattern. In Chongqing, AHF concentrated in several hotspots of the (sub)centers, resulting in a balanced and clustered pattern. AHF from buildings and human metabolism were contiguous and concentrated within city centers. In contrast, AHF from traffic and industries were scattered and dispersed in line with the road networks and industrial patterns. The results reflected that the spatial pattern of AHF varied with different urban forms, which called for planners to consider the differences and make related policies to mitigate AHF for urban sustainability.

A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high-spatial-resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. Firstly, the observed environmental parameters, e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF), around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 ∘C. Then, the spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the decreasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high spatial and temporal resolution (30 m and 1 h) CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

Improved anthropogenic heat flux model for fine spatiotemporal information in Southeast China

Anthropogenic heat emission (AHE) is an important driver of urban heat islands (UHIs). Further, both urban thermal environment research and sustainable development planning require an efficient estimation of anthropogenic heat flux (AHF). Therefore, this study proposed an improved multi-source AHF model, which was constructed using diverse data sources and small-scale samples, to better represent the spatiotemporal distribution of AHF. The performances of three machine learning algorithms (Cubist, gradient boosting decision tree, and simple linear regression) were quantitatively evaluated, and the impact of spatiotemporal heterogeneity on AHF estimation was considered for the first time. The results showed that multi-source datasets and sophisticated algorithms could more effectively reduce the estimation error and improve the accuracy of the spatiotemporal distribution of AHF than simple linear regression. In practical applications, the Cubist model performed better, with prediction errors being less than 0.9 W⋅m−2. Further, the characteristics of different heat sources from the model outputs varied widely, and the building metabolic heat exhibited significant seasonal spatiotemporal variations, which were largely determined by the regional climate. In contrast, industrial and transportation heat showed marginal monthly fluctuations. Similarly, spatiotemporal heterogeneity significantly affected the estimation of building metabolic heat (0.62 W⋅m−2), but it did not affect other heat sources. The proposed improved AHF model was verified to effectively capture the spatiotemporal variations of building heat and solve the issue of overestimation of industrial heat in urban regions. This study provides new methods and ideas for the accurate spatiotemporal quantification of AHF that can supplement future studies on climate warming, UHI, and air pollution.