Quantifying the seasonal contribution of coupling urban land use types on Urban Heat Island using Land Contribution Index: A case study in Wuhan, China

Huang, Qiuping; Huang, Jikun; Yang, Xining; Fang, Chuanglin; Liang, Youjia

doi:10.1016/j.scs.2018.10.016

Cited by 96 publications

(42 citation statements)

References 37 publications

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“…It is reasonable as cooling effects are normally expected at places with lower albedo under the absence or near-absence of evaporation [73]. The weakest cooling effect of water bodies in winter was also in accordance with previous studies [21,74]. It is interesting to find that water bodies tended to decrease LST in a global manner, especially for the transition season and winter.…”

Section: The Local Landscape Influence and Its Seasonal Variationsupporting

confidence: 90%

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

et al. 2019

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There has been a growing concern for the urbanization induced local warming, and the underlying mechanism between urban thermal environment and the driving landscape factors. However, relatively little research has simultaneously considered issues of spatial non-stationarity and seasonal variability, which are both intrinsic properties of the environmental system. In this study, the newly proposed multi-scale geographically weighted regression (MGWR) is employed to investigate the seasonal variations of the spatial non-stationary associations between land surface temperature (LST) and urban landscape indicators under different operating scales. Specifically, by taking Wuhan as a case study, Landsat-8 images were used to achieve the LSTs in summer, winter and the transitional season, respectively. Landscape composition indicators including fractional vegetation cover (FVC), albedo and water percentage (WP) and urban morphology indicators covering building density (BD), building height (BH) and building volume density (BVD) were employed as potential landscape drivers of LST. For reference, the conventional geographically weighted regression (GWR) and ordinary least squares (OLS) regression were also employed. Results revealed that MGWR outperformed GWR and OLS in terms of goodness-of-fit for all seasons. For the specific associations with LST, all six indicators exhibited evident seasonal variations, especially from the transition season to winter. FVC, albedo and BD were observed to possess great spatial non-stationarity for all seasons, while WP, BH and BD tended to influence LST globally. Overall, FVC exhibited certain positive effect in winter. The negative effect of WP was the greatest among all indicators, although it became the weakest in winter. Albedo tended to influence LST more complicatedly than simple cooling. BD, with a consistent heating effect, was testified to have a greater influence on LST than BH for all seasons. The BH-LST association tended to transfer into positive in winter, while the BVD-LST association remained negative for all seasons. The results could support the establishment of season- and site-specific mitigation strategies. Generally, this study facilitates our understanding of human-environment interaction and narrows the gap between climate research and city management.

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Section: The Local Landscape Influence and Its Seasonal Variationsupporting

confidence: 90%

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

et al. 2019

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“…China's urbanization rate increased by approximately 40% in 30 years [63]. Through urbanization, urban land cover has dramatically changed, which has had a serious effect on urban microclimates and caused environmental deterioration [64][65][66]. This long-term observational study illustrated that Wuhan has been increasingly urbanized and UHII has become more serious and has dramatically altered heat balance and effects on thermal comfort.…”

Section: Discussionmentioning

confidence: 99%

The Interactive Impact of Building Diversity on the Thermal Balance and Micro-Climate Change under the Influence of Rapid Urbanization

Makvandi

Elsadek

et al. 2019

Sustainability

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Numerous cities face the serious problems of rapid urbanization and climate change, especially in recent years. Among all cities, Wuhan is one of the most affected by these changes, accompanied by the transformation of water surfaces into urban lands and the decline of natural ventilation. This study investigated the impact of surface urban heat island enlargement (SUHI) and block morphology changes in heat balance. Accordingly, the interactive impact of building diversity with major building forms (low-rise, mid-rise, and high-rise) on thermal balance and microclimate changes under the influence of urban land expansion at the residential block scale was studied. To investigate the heat balance changes by air temperature intensification and air movement reduction, a long-term and field observational analysis (1980–2018) coupled with computational fluid dynamic simulation (CFD) was used to evaluate the impact of building diversity on thermal balance. Outcomes show that urban heat island intensity (UHII) increased by 2 ℃ when water surfaces in urban areas decreased; consequently, there was a deterioration in the air movement to alleviate UHII. Thus, the air movement declined substantially with UHII and SUHI enlargement, which, through increased urban surfaces and roughness length, will become worse by 2020. Furthermore, the decline in air movement caused by the transformation of urban water bodies cannot contribute to the heat balance unless reinforced by the morphology of the urban blocks. In the design of inner-city blocks, morphological indicators have a significant impact on microclimate and heat balance, where increasing building density and plot ratio will increase UHII, and increasing water surfaces will result in an increase in urban ventilation. Lastly, a substantial correlation between air temperature and relative humidity was found, which, together with the block indicators, can help control the air temperature and adjust the urban microclimate.

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“…Diverse factors affect the temperature in a region, such as the population, carbon dioxide emissions, land use, and land cover change [21][22][23]. The interactions between them are too complex to establish with a systematic correlation.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature

et al. 2019

Self Cite

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Temperature forecasting is a crucial part of climate change research. It can provide a valuable reference, as well as practical significance, for understanding the macroscopic evolutionary processes of regional temperature and for promoting sustainable development. This study presents a new integrated model, called the Variational Mode Decomposition-Autoregressive Integrated Moving Average (VMD-ARIMA) model, which reduces the required data input and improves the accuracy of predictions, based on the deficiencies of data dependence and the complicated mechanisms associated with current temperature forecasting. In this model, the variational mode decomposition (VMD) was used for mining the trend features and detailed features contained in a time series, as well as denoising. Moreover, the corresponding autoregressive integrated moving average (ARIMA) models were derived to reflect the different features of the components. The final forecasted values were then obtained using VMD reconstruction. The annual temperature time series from the Wuhan Meteorological Station were investigated using the VMD-ARIMA model, ARIMA model, and Grey Model (1, 1) based on three statistical performance metrics (mean relative error, mean absolute error, and root mean square error). The results indicate that the VMD-ARIMA model can effectively enhance the accuracy of temperature forecasting.

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Quantifying the seasonal contribution of coupling urban land use types on Urban Heat Island using Land Contribution Index: A case study in Wuhan, China

Cited by 96 publications

References 37 publications

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

The Interactive Impact of Building Diversity on the Thermal Balance and Micro-Climate Change under the Influence of Rapid Urbanization

An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature

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