Seasonal different effects of land cover on urban heat island in Wuhan's metropolitan area

Yuan, Shihui; Ren, Zhiyuan; Shan, Xiaofang; Deng, Qinli; Zhou, Zeng

doi:10.1016/j.uclim.2023.101547

Cited by 24 publications

(5 citation statements)

References 23 publications

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“…To prevent future deterioration of the urban environment, better protection of cold areas such as rivers and lakes [55,56] should be an important consideration in the upcoming urban construction process. According to the existing development trend, the area of building land would grow in the future while the area of farmland would continue to decline.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Urban Heat Island Effect in Wuhan Urban Area Based on Prediction of Urban Underlying Surface Coverage Type Change

Zuo,

Ren,

Shan

et al. 2024

Advances in Meteorology

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The rapid development of urbanization makes the phenomenon of urban heat islands even more serious. Predicting the impact of land cover change on urban heat island has become one of the research hotspots. Taking Wuhan, China, as an example, this study simulated the land type change in 2020 through the Cellular Automata-Markov-Chain (CA-Markov) model. The urban heat island in 2020 was simulated and analyzed in conjunction with the Weather Research & Forecasting Model (WRF), and the simulation results of wind velocity and temperature were confirmed using weather station observation data. Based on this, the land cover and urban heat island of Wuhan in 2030 were predicted. The temperature was found to be well-fit by CA-Markov simulated land use data, with an average inaccuracy of about 2.5°C for weather stations. Wind speed had a poor fitting effect; the average error was roughly 2 m/s. The built-up area was the center of the high temperature area both before and after the prediction, the water was the low temperature area, and the peak heat island happened at night. According to the forecast results, there will be more built-up land in 2030, and there will be a greater intensity of heat islands than in 2020.

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Section: Discussionmentioning

confidence: 99%

Analysis of Urban Heat Island Effect in Wuhan Urban Area Based on Prediction of Urban Underlying Surface Coverage Type Change

Zuo,

Ren,

Shan

et al. 2024

Advances in Meteorology

Self Cite

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“…The high %CV values were found at higher latitudes of Thailand (the upper area of Thailand from the central to the northern region), especially in the northern region due to much of the land area, including high mountain ranges and valleys, being a long way from the sea with varying altitude [60], leading to large differences in temperature in different seasons. Moreover, land can absorb heat from solar radiation faster, although land can also release the heat faster by the transpiration and convection processes in the day and night time, leading to the surrounding temperature dropping by more than in the urban areas [61,62]. Furthermore, the influence of the power of the northeast monsoon in winter, which extended variably to higher latitude areas each year, caused the temperature in the upper part of Thailand to fluctuate [63].…”

Section: Temperature During 1987 To 2021mentioning

confidence: 99%

Investigation of Temperature, Precipitation, Evapotranspiration, and New Thornthwaite Climate Classification in Thailand

Phumkokrux,

Trivej

2024

Atmosphere

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This study aims (1) to study the trend and characteristics of average annual air temperature (Tann), annual precipitation (Prann), and annual evapotranspiration (PETann) in Thailand over the present period (1987–2021) and (2) to extract the climate pattern in form of a map using the New Thornthwaite Climate Classification method in Thailand considering the present period. The data were prepared by the Thai Meteorological Department. Data variability, the mean of the data calculation in time series, the homogeneity test of data, and abrupt changes were examined. The trends of each variable were calculated using the Mann–Kendal and Sen’s slope test. The results indicated that the high Tann found in Bangkok gradually decreased in the next area. Tann data were heterogeneous with the abrupt change period, and increasing trends were found. Prann values were high in the west side of the southern area and the bottom area of the eastern area; in addition, low rainfall was found in the inner area of the land. Prann data were homogenous with no abrupt change period and slight changes in trends. PETann and %CV spatial distribution were determined for the same pattern of Tann. PETann data were heterogeneous with abrupt change periods and rising trends. The torrid thermal index determined based on the New Thornthwaite Climate Classification results indicated an overall torrid-type climate. A semi-arid climate pattern was found in the small area of the middle of Thailand, and then it shifted toward a moist-type pattern in the next area with an in precipitation. The most climate variability was found to be extreme with the power of temperature changes.

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“…Since urban development and climate change are both dynamic processes involving numerous elements, these elements are interrelated and mutually influential. Therefore, in addition to studying the impact of climate change on urban development, the stress effects of urbanization on the climate have also received widespread attention, e.g., urban surface warming [23,24], the urban heat island effect [25][26][27], and the urban rain island effect [28,29], among others. In summary, the relationship between urban development and climate change is one of mutual influence and constraint.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatial and Temporal Changes and Drivers of Urban Sprawl in Xinjiang Based on Integrated DMSP-OLS and NPP-VIIRS Data

Wang,

Xu,

Xue

et al. 2024

Land

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The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution.

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Seasonal different effects of land cover on urban heat island in Wuhan's metropolitan area

Cited by 24 publications

References 23 publications

Analysis of Urban Heat Island Effect in Wuhan Urban Area Based on Prediction of Urban Underlying Surface Coverage Type Change

Analysis of Urban Heat Island Effect in Wuhan Urban Area Based on Prediction of Urban Underlying Surface Coverage Type Change

Investigation of Temperature, Precipitation, Evapotranspiration, and New Thornthwaite Climate Classification in Thailand

Analysis of Spatial and Temporal Changes and Drivers of Urban Sprawl in Xinjiang Based on Integrated DMSP-OLS and NPP-VIIRS Data

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