Spatiotemporal characteristics of anthropogenic heat in an urban environment: A case study of Tsinghua Campus

Nie, Wanshu; Sun, Ting; Ni, Guangheng

doi:10.1016/j.buildenv.2014.10.011

Cited by 42 publications

(34 citation statements)

References 31 publications

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“…In the densest parts of cities, the anthropogenic heat (Q F ) often has a large influence on the SEB and it needs to be accounted for (Allen et al, 2011;Chow et al, 2014;Nie et al, 2014;Sailor, 2011). This requires the governing SEB relation (Eq.…”

Section: Derivation Of Anohm Coefficientsmentioning

confidence: 99%

The Analytical Objective Hysteresis Model (AnOHM v1.0): methodology to determine bulk storage heat flux coefficients

et al. 2017

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Abstract. The net storage heat flux ( Q S ) is important in the urban surface energy balance (SEB) but its determination remains a significant challenge. The hysteresis pattern of the diurnal relation between the Q S and net all-wave radiation (Q * ) has been captured in the Objective Hysteresis Model (OHM) parameterization of Q S . Although successfully used in urban areas, the limited availability of coefficients for OHM hampers its application. To facilitate use, and enhance physical interpretations of the OHM coefficients, an analytical solution of the one-dimensional advectiondiffusion equation of coupled heat and liquid water transport in conjunction with the SEB is conducted, allowing development of AnOHM (Analytical Objective Hysteresis Model). A sensitivity test of AnOHM to surface properties and hydrometeorological forcing is presented using a stochastic approach (subset simulation). The sensitivity test suggests that the albedo, Bowen ratio and bulk transfer coefficient, solar radiation and wind speed are most critical. AnOHM, driven by local meteorological conditions at five sites with different land use, is shown to simulate the Q S flux well (RMSE values of ∼ 30 W m −2 ). The intra-annual dynamics of OHM coefficients are explored. AnOHM offers significant potential to enhance modelling of the surface energy balance over a wider range of conditions and land covers.

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Section: Derivation Of Anohm Coefficientsmentioning

confidence: 99%

The Analytical Objective Hysteresis Model (AnOHM v1.0): methodology to determine bulk storage heat flux coefficients

et al. 2017

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“…Although there is a time lag between energy consumption and actual heat emissions released into the atmosphere, due to the lack of accurate energy consumption efficiency data, this study makes the same assumption as previous studies: the energy consumption eventually translates into anthropogenic sensible heat with no time lag or consideration for environmental loads, and particular locations that emit latent heat such as cooling towers are not taken into account [17,34,35]. The energy-consumption inventory approach is time-consuming and labor-intensive for AHF estimation over a large area.…”

Section: Estimation Of County-scale Annual Mean Ahf With a Top-down Ementioning

confidence: 99%

Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data

Chen

2017

Remote Sensing

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Anthropogenic heat (AH) generated by human activities is an important factor affecting the urban climate. Thus, refined AH parameterization of a large area can provide data support for regional meteorological research. In this study, we developed a refined anthropogenic heat flux (RAHF) parameterization scheme to estimate the gridded anthropogenic heat flux (AHF). Firstly, the annual total AH emissions and annual mean AHF of Beijing municipality in the year 2015 were estimated using a top-down, energy-consumption inventory method, which was derived based on socioeconomic statistics and energy consumption data. The heat released from industry, transportation, buildings (including both commercial and residential buildings), and human metabolism were taken into account. Then, the county-scale AHF estimation model was constructed based on multi-source remote sensing data, such as Suomi national polar-orbiting partnership (Suomi-NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light (NTL) data and moderate resolution imaging spectroradiometer (MODIS) data. This model was applied to estimate the annual mean AHF of the counties in the Beijing-Tianjin-Hebei region. Finally, the gridded AHF data with 500-m resolution was obtained using a RAHF parameterization scheme. The results indicate that the annual total AH emissions of Beijing municipality in the year 2015 was approximately 1.704 × 10 18 J. Of this, the buildings contribute about 34.5%, followed by transportation and industry with about 30.5% and 30.1%, respectively, and human metabolism with only about 4.9%. The annual mean AHF value of the Beijing-Tianjin-Hebei region is about 6.07 W·m −2 , and the AHF in urban areas is about in the range of 20 W·m −2 and 130 W·m −2 . The maximum AHF value is approximately 130.84 W·m −2 , mostly in airports, railway stations, central business districts, and other densely-populated areas. The error analysis of the county-scale AHF results showed that the residual between the model estimation and energy consumption statistics is less than 1%. In addition, the spatial distribution of RAHF results is generally centered on urban area and gradually decreases towards suburbs. The spatial pattern of the RAHF results within urban areas corresponds well to the distribution of population density, building density, and the industrial district. The spatial heterogeneity of AHF within urban areas is well-reflected through the RAHF results. The RAHF results can be used in meteorological and environmental modeling for the Beijing-Tianjin-Hebei region. The results of this study also highlight the superiority of Suomi-NPP VIIRS NTL data for AHF estimation.

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“…High anthropogenic heat is generally observed in Beijing and in the densely built-up areas the hourly maximum value even as high as 474.3 W m -2 . (Nie et al, 2014;Tong et al, 2004). In this section, anthropogenic heat was quantified to estimate ET in Beijing by a modified SEBS model.…”

Section: Sebs-urban Modelmentioning

confidence: 99%

“…In this study, the threshold value was defined as 52 for separating the anthropogenic heat-impacted areas from the anthropogenic heat-free areas (Shu et al, 2011). The values of anthropogenic heat were set as a range from 50 to 75 W m −2 for summer and winter, and 30 W m −2 to 50 W m −2 for spring and autumn, on the basis of researches conducted by Nie et al (2014) and Tong et al (2004). Then the corresponding light intensity limits were 52 and 63 and the internal values were produced by linear interpolation.…”

Section: Sebs-urban Modelmentioning

confidence: 99%

Evapotranspiration estimation considering anthropogenic heat based on remote sensing in urban area

Cong

Shen

Zhou

et al. 2017

Sci. China Earth Sci.

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Urbanization influences hydrologic cycle significantly on local, regional even global scale. With urbanization the water resources demand for dense population sharpened, thus it is a great challenge to ensure water supply for some metropolises such as Beijing. Urban area is traditionally considered as the area with lower evapotranspiration (ET) on account of the impervious surface and the lower wind speed. For most remote sensing models, the ET, defined as latent heat in energy budget, is estimated as the difference between net radiation and sensible heat. The sensible heat is generally higher in urban area due to the high surface temperature caused by heat island, therefore the latent heat (i.e. the ET) in urban area is lower than that in other region. We estimated water consumption from 2003 to 2012 in Beijing based on water balance method and found that the annual mean ET in urban area was about 654 mm. However, using Surface Energy Balance System (SEBS) model, the annual mean ET in urban area was only 348 mm. We attributed this inconsistence to the impact of anthropogenic heat and quantified this impact on the basis of the night-light maps. Therefore, a new model SEBS-Urban, coupling SEBS model and anthropogenic heat was developed to estimate the ET in urban area. The ET in urban area of Beijing estimated by SEBS-Urban showed a good agreement with the ET from water balance method. The findings from this study highlighted that anthropogenic heat should be included in the surface energy budget for a highly urbanized area.

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Spatiotemporal characteristics of anthropogenic heat in an urban environment: A case study of Tsinghua Campus

Cited by 42 publications

References 31 publications

The Analytical Objective Hysteresis Model (AnOHM v1.0): methodology to determine bulk storage heat flux coefficients

The Analytical Objective Hysteresis Model (AnOHM v1.0): methodology to determine bulk storage heat flux coefficients

Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data

Evapotranspiration estimation considering anthropogenic heat based on remote sensing in urban area

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