Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

Abstract

For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.

Where to Go or Where Not to Go—A Method for Advising Communities during Extreme Temperatures

“…Thus, these maps provide information on the places where people should or should not go during extreme weather events (e.g., a heat wave, cold wave, strong winds, marine storm surges, and/or intense precipitation). In fact, climate risks can only be dealt with by three approaches: risk management, mitigation, or adaptation [15,35,[37][38][39][40][41][42][43]. In the first and second cases, we need to implement actions that reduce all types of risks related to climate change in regard to the diversity of people and place combinations in each neighbourhood.…”

Theo dõi đảo nhiệt bề mặt đô thị tại thành phố Cần Thơ giai đoạn 2014-2020

“…Về cơ bản, nhiệt độ khu vực đô thị ngày càng tăng cao do nhiệt được lưu trữ và bức xạ trở lại bởi các công trình đô thị có quy mô lớn và phức tạp. UHI gây ra các tác động tiêu cực như làm nóng bề mặt của cảnh quan đô thị, gia tăng nhu cầu năng lượng và phát thải CO 2 , làm suy yếu môi trường sống, sức khỏe cộng đồng, gia tăng tỷ lệ tử vong và gây ảnh hưởng đối với đời sống của những người sống trong đô thị (Rizwan, 2008;Wu et al, 2012;Mohajerani et al, 2017;Tong et al, 2017;Chen et al, 2020;Yang et al, 2020). Đảo nhiệt bề mặt đô thị (surface urban heat island-SUHI) và UHI khí quyển là hai loại UHI (Asgarian et al, 2015).…”

Spatial–temporal characteristics and driving factors of flash floods in Shaanxi Province considering regional differentiation

“…The HAF included the population density (PD; unit: person/km 2 ), the density of gross domestic product (GDP; unit: 10 4 yuan/km 2 ), the density of villages along the river point (VD; unit: village/km 2 ), and the land use/land cover change (LUCC; unitless). The LUCC was included in the HAF since the LUCC is mainly affected by human activities (Chen et al 2020;Yu et al 2020).…”