scholarly journals A New Urban Functional Zone-Based Climate Zoning System for Urban Temperature Study

2021 ◽  
Vol 13 (2) ◽  
pp. 251
Author(s):  
Zhaowu Yu ◽  
Yongcai Jing ◽  
Gaoyuan Yang ◽  
Ranhao Sun
Keyword(s):  
Land Surface ◽  
Urban Climate ◽  
Anthropogenic Heat ◽  
City Management ◽  
Spatiotemporal Resolution ◽  
Comparison Theory ◽  
Effective Measure ◽  
Functional Zone ◽  
Climate Studies ◽  
Temperature Zoning

The urban heat island (UHI) effect has been recognized as one of the most significant terrestrial surface climate-related consequences of urbanization. However, the traditional definition of the urban–rural (UR) division and the newly established local climate zone (LCZ) classification for UHI and urban climate studies do not adequately express the pattern and intensity of UHI. Moreover, these definitions of UHI find it hard to capture the human activity-induced anthropogenic heat that is highly correlated with urban functional zones (UFZ). Therefore, in this study, with a comparison (theory, technology, and application) of the previous definition (UR and LCZ) of UHI and integration of computer programming technology, social sensing, and remote sensing, we develop a new urban functional zone-based urban temperature zoning system (UFZC). The UFZC system is generally a social-based, planning-oriented, and data-driven classification system associated with the urban function and temperature; it can also be effectively used in city management (e.g., urban planning and energy saving). Moreover, in the Beijing case, we tested the UFZC system and preliminarily analyzed the land surface temperature (LST) difference patterns and causes of the 11 UFZC types. We found that, compared to other UFZCs, the PGZ (perseveration green zone)-UFZC has the lowest LST, while the CBZ (center business district zone)-UFZC and GCZ (general commercial zone)-UFZC contribute the most and stable heat sources. This implies that reducing the heat generated by the function of commercial (and industrial) activities is an effective measure to reduce the UHI effect. We also proposed that multi-source temperature datasets with a high spatiotemporal resolution are needed to obtain more accurate results; thus providing more accurate recommendations for mitigating UHI effects. In short, as a new and finer urban temperature zoning system, although UFZC is not intended to supplant the UR and LCZ classifications, it can facilitate more detailed and coupled urban climate studies.

scholarly journals Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood scale

2021 ◽  
Vol 21 (17) ◽  
pp. 13687-13711
Author(s):  
Michael Biggart ◽  
Jenny Stocker ◽  
Ruth M. Doherty ◽  
Oliver Wild ◽  
David Carruthers ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Land Surface ◽  
Urban Climate ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Canopy Layer ◽  
Near Surface ◽  
Air Temperatures ◽  
Urban Heat ◽  
Neighbourhood Scale

Abstract. Information on the spatiotemporal characteristics of Beijing's urban–rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (∼ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both local climate zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ∼ 4.5 ∘C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher-resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.

scholarly journals Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood-scale

2020 ◽  
Author(s):  
Michael Biggart ◽  
Jenny Stocker ◽  
Ruth M. Doherty ◽  
Oliver Wild ◽  
David Carruthers ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Land Surface ◽  
Urban Climate ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Canopy Layer ◽  
Near Surface ◽  
Air Temperatures ◽  
Urban Heat ◽  
Neighbourhood Scale

Abstract. Information on the spatiotemporal characteristics of Beijing's urban-rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (~ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both Local Climate Zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ~ 4.5 °C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.

scholarly journals Advantages of using a fast urban canopy model as compared to a full mesoscale model to simulate the urban heat island of Barcelona

2016 ◽  
Author(s):  
M. García-Díez ◽  
D. Lauwaet ◽  
H. Hooyberghs ◽  
J. Ballester ◽  
K. De Ridder ◽  
...  
Keyword(s):  
Simple Model ◽  
Land Surface ◽  
Climate Model ◽  
Mesoscale Model ◽  
Urban Climate ◽  
Urban Canopy ◽  
Forecast Model ◽  
Reanalysis Data ◽  
Urban Canopy Model ◽  
Canopy Model

Abstract. As most of the population lives in urban environments, the simulation of the urban climate has become a key problem in the framework of the climate change impact assessment. However, the high computational power required by these simulations is a severe limitation. Here we present a study on the performance of a Urban Climate Model (UrbClim), designed to be several orders of magnitude faster than a full-fledge mesoscale model. The simulations are validated with station data and with land surface temperature observations retrieved by satellites. To explore the advantages of using a simple model like UrbClim, the results are compared with a simulation carried out with a state-of-the-art mesoscale model, the Weather Research and Forecasting model, using an Urban Canopy model. The effect of using different driving data is explored too, by using both relatively low resolution reanalysis data (70 km) and a higher resolution forecast model (15 km). The results show that, generally, the performance of the simple model is comparable to or better than the mesoscale model. The exception are the winds and the day-to-day correlation in the reanalysis driven run, but these problems disappear when taking the boundary conditions from the higher resolution forecast model.

scholarly journals Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

2021 ◽  
Vol 13 (21) ◽  
pp. 4465
Author(s):  
Yu Shen ◽  
Xiaoyang Zhang ◽  
Weile Wang ◽  
Ramakrishna Nemani ◽  
Yongchang Ye ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Cloud Cover ◽  
Landsat 8 ◽  
Field Scale ◽  
Spatiotemporal Resolution ◽  
Essential Information ◽  
Land Surface Phenology ◽  
The Earth ◽  
Sentinel 2

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

scholarly journals Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network

2019 ◽  
Vol 11 (9) ◽  
pp. 1091 ◽  
Author(s):  
Lu Li ◽  
Chao Wang ◽  
Hong Zhang ◽  
Bo Zhang ◽  
Fan Wu
Keyword(s):  
Change Detection ◽  
Rapid Development ◽  
Ratio Method ◽  
Small Scale ◽  
City Management ◽  
Spatiotemporal Resolution ◽  
Sar Images ◽  
Difference Image ◽  
Radar Images ◽  
Log Ratio

With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method.

scholarly journals Learning to Chill: The Role of Design Schools and Professional Training to Improve Urban Climate and Urban Metabolism

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2243 ◽  
Author(s):  
Mohammad Taleghani ◽  
Azadeh Montazami ◽  
Daniela Perrotti
Keyword(s):  
Higher Education ◽  
Professional Training ◽  
Urban Climate ◽  
Field Measurements ◽  
Urban Environments ◽  
Urban Heat Islands ◽  
Urban Metabolism ◽  
Mortality And Morbidity ◽  
Urban Heat ◽  
Climate Studies

The increased frequency of heat-related mortality and morbidity in urban environments indicates the importance of urban climate studies. As most of the world’s population lives in cities, the education of designers, planners and policy makers is crucial to promote urban sustainability This paper, firstly, focuses on the different factors causing the urban heat islands in large cities. Secondly, it considers how these factors are reflected in higher education programmes. Examples are shown from courses in UK higher education, explaining the common software tools used for simulating urban spaces, and student field measurements are drawn on to illustrate how urban climate studies are included in higher education curricula. Urban metabolism is used to conceptualise the main approach to systemic resource-use assessments and as a holistic framework to investigate the main drivers of the urban heat island phenomenon. To sum up, this paper reflects on the importance of training climatically-aware graduates from design schools.

scholarly journals Impact of Insolation Data Source on Remote Sensing Retrievals of Evapotranspiration over the California Delta

2019 ◽  
Vol 11 (3) ◽  
pp. 216 ◽  
Author(s):  
Martha Anderson ◽  
George Diak ◽  
Feng Gao ◽  
Kyle Knipper ◽  
Christopher Hain ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Land Surface ◽  
Water Resource Management ◽  
Radiant Energy ◽  
Data Sources ◽  
Delta Region ◽  
Spatiotemporal Resolution ◽  
Sky Conditions ◽  
Mean Square Errors

The energy delivered to the land surface via insolation is a primary driver of evapotranspiration (ET)—the exchange of water vapor between the land and atmosphere. Spatially distributed ET products are in great demand in the water resource management community for real-time operations and sustainable water use planning. The accuracy and deliverability of these products are determined in part by the characteristics and quality of the insolation data sources used as input to the ET models. This paper investigates the practical utility of three different insolation datasets within the context of a satellite-based remote sensing framework for mapping ET at high spatiotemporal resolution, in an application over the Sacramento–San Joaquin Delta region in California. The datasets tested included one reanalysis product: The Climate System Forecast Reanalysis (CFSR) at 0.25° spatial resolution, and two remote sensing insolation products generated with geostationary satellite imagery: a product for the continental United States at 0.2°, developed by the University of Wisconsin Space Sciences and Engineering Center (SSEC) and a coarser resolution (1°) global Clouds and the Earth’s Radiant Energy System (CERES) product. The three insolation data sources were compared to pyranometer data collected at flux towers within the Delta region to establish relative accuracy. The satellite products significantly outperformed CFSR, with root-mean square errors (RMSE) of 2.7, 1.5, and 1.4 MJ·m−2·d−1 for CFSR, CERES, and SSEC, respectively, at daily timesteps. The satellite-based products provided more accurate estimates of cloud occurrence and radiation transmission, while the reanalysis tended to underestimate solar radiation under cloudy-sky conditions. However, this difference in insolation performance did not translate into comparable improvement in the ET retrieval accuracy, where the RMSE in daily ET was 0.98 and 0.94 mm d−1 using the CFSR and SSEC insolation data sources, respectively, for all the flux sites combined. The lack of a notable impact on the aggregate ET performance may be due in part to the predominantly clear-sky conditions prevalent in central California, under which the reanalysis and satellite-based insolation data sources have comparable accuracy. While satellite-based insolation data could improve ET retrieval in more humid regions with greater cloud-cover frequency, over the California Delta and climatologically similar regions in the western U.S., the CFSR data may suffice for real-time ET modeling efforts.

scholarly journals Anthropogenic Heat Flux Estimation Based on Luojia 1-01 New Nighttime Light Data: A Case Study of Jiangsu Province, China

2020 ◽  
Vol 12 (22) ◽  
pp. 3707
Author(s):  
Zhongli Lin ◽  
Hanqiu Xu
Keyword(s):  
Heat Flux ◽  
Energy Consumption ◽  
Urban Climate ◽  
Power Estimation ◽  
Anthropogenic Heat ◽  
Economic Activities ◽  
Estimation Model ◽  
Nighttime Light Data ◽  
Anthropogenic Heat Flux ◽  
Nighttime Light

With the rapid process of urbanization, anthropogenic heat generated by human activities has become an important factor that drives the changes in urban climate and regional environmental quality. The nighttime light (NTL) data can aptly reflect the spatial distribution of social-economic activities and energy consumption, and quantitatively estimate the anthropogenic heat flux (AHF) distribution. However, the commonly used DMSP/OLS and Suomi-NPP/VIIRS NTL data are restricted by their coarse spatial resolution and, therefore, cannot exhibit the spatial details of AHF at city scale. The 130 m high-resolution NTL data obtained by Luojia 1-01 satellite launched in June 2018 shows a promise to solve this problem. In this paper, the gridded AHF spatial estimation is achieved with a resolution of 130 m using Luojia 1-01 NTL data based on three indexes, NTLnor (Normalized Nighttime Light Data), HSI (Human Settlement Index), and VANUI (Vegetation Adjusted NTL Urban Index). We chose Jiangsu, a fast-developing province in China, as an example to determine the best AHF estimation model among the three indexes. The AHF of 96 county-level cities of the province was first calculated using energy-consumption statistics data and then correlated with the corresponding data of three indexes. The results show that based on a 5-fold cross-validation approach, the VANUI power estimation model achieves the highest R2 of 0.8444 along with the smallest RMSE of 4.8277 W·m−2 and therefore has the highest accuracy among the three indexes. According to the VANUI power estimation model, the annual mean AHF of Jiangsu in 2018 was 2.91 W·m−2. Of the 96 cities, Suzhou has the highest annual mean AHF of 7.41 W·m−2, followed by Wuxi, Nanjing, Changzhou and Zhenjiang, with the annual mean of 3.80–5.97 W·m−2, while the figures of Suqian, Yancheng, Lianyungang, and Huaian, the cities in northern Jiangsu, are relatively low, ranging from 1.41 to 1.59 W·m−2. This study has shown that the AHF estimation model developed by Luojia 1-01 NTL data can achieve higher accuracy at city-scale and discriminate the spatial detail of AHF effectively.

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