Spatiotemporal Distribution and Risk Assessment of Heat Waves Based on Apparent Temperature in the One Belt and One Road Region

Yin, Cong; Yang, Fei; Wang, Juanle; Ye, Yexing

doi:10.3390/rs12071174

Cited by 24 publications

(24 citation statements)

References 66 publications

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“…By comparing the results with the 2015 global standard heat wave index distribution of Raei [50] and the Belt and Road regional HWR assessment of Yin [21], it is concluded that the regional HWR at the kilometer scale agrees well with that determined in previous large-scale studies in terms of the overall dynamics. The risk is higher in the northwestern part of the study area and the highest in the Jakarta region.…”

Section: Comparative Analysis Of the Heat Wave Risk Resultssupporting

confidence: 72%

“…However, all of these indicators mentioned, including those considered relevant but not mentioned, need to be explored and will be considered for inclusion in future updates of this study once city-level or more detailed-level data are available. (3) Heat waves can be fatal in severe cases, so the number of heat-related deaths or the incidence of heat-related mortality best validates the HWR [5,21], but these data are currently difficult to obtain. The extraction of heat-related mortality data from all-cause mortality data and mortality data related to various diseases will be a key focus of future HWR studies.…”

Section: Discussionmentioning

confidence: 99%

“…(5) The importance of each assessment factor (population of elderly individuals, population of young individuals, economic level, hospital/road distribution, vegetation/water coverage, impervious surface) was not differentiated when conducting the vulnerability assessment. In fact, each factor contributes differently to the heat wave risk [21,41]. However, there is currently no uniformity in the determination of the weights of each indicator in heat wave risk evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…A review of the current literature on HWR assessment worldwide reveals the following problems: (1) In terms of the time scale, most of the studies on HWR changes at long time scales are based on meteorological station data, and only indicators of the heat wave intensity, frequency and duration are chosen [17][18][19][20][21], or indicators such as the effective cumulative temperature calculated considering meteorological station temperature data [22] to study heat waves in a particular region over decades. However, studies based on meteorological station data consider only one station to represent the temperature of the whole city, or when the study area is large, the variability in heat waves is mostly studied via interpolation of station-measured temperatures [20,21,23]. Although the above methods can suitably analyze changes over time on a yearly scale, the spatial scale is coarse (10-50 km), and only analyses from the perspective of heat wave hazards have been conducted.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spatiotemporal Variation Analysis of the Fine-Scale Heat Wave Risk along the Jakarta-Bandung High-Speed Railway in Indonesia

Dai

Liu

Huang

et al. 2021

IJERPH

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As a highly important meteorological hazard, heat waves notably impact human health and socioeconomics, and accurate heat wave risk identification and assessment are effective ways to address this issue. The current spatial scale of heat wave risk assessment is relatively coarse, hardly meeting fine-scale heat wave risk assessment requirements. Therefore, based on multi-source fine-scale remote sensing data and socioeconomic data, this paper evaluates the heat wave risk along the Jakarta-Bandung high-speed railway, obtains the spatial distribution of heat wave risk in 2005, 2014 and 2019, and analyzes spatiotemporal risk variations over the past 15 years. The results show that most high-risk areas were affected by high-temperature hazards. Over time, the hazard, exposure, vulnerability and risk levels increased by 25.82%, 3.31%, 14.82% and 6.97%, respectively, from 2005–2019. Spatially, the higher risk in the northwest is mainly distributed in Jakarta. Additionally, a comparative analysis was conducted on the risk results, and the results showed that the 100-m scale showed more spatial differences than the kilometer scale. The research results in this paper can provide scientific advice on heat wave risk prevention considering the Jakarta-Bandung high-speed railway construction and regional economic and social development.

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Section: Comparative Analysis Of the Heat Wave Risk Resultssupporting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatiotemporal Variation Analysis of the Fine-Scale Heat Wave Risk along the Jakarta-Bandung High-Speed Railway in Indonesia

Dai

Liu

Huang

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…In recent years, temperature change in China as a result of climate change has attracted a great deal of attention. Many studies revealed changes in temperature and extreme weather events over the past few decades [5][6][7][8]. Their choice of heat threshold has generally followed two approaches: (1) exceedance of fixed absolute values (e.g., 35 • C) or (2) deviation from normal.…”

Section: Introductionmentioning

confidence: 99%

A New Method to Estimate Heat Exposure Days and Its Impacts in China

et al. 2021

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Understanding the spatiotemporal trends of temperature in the context of global warming is significant for public health. Although many studies have examined changes in temperature and the impacts on human health over the past few decades in many regions, they have often been carried out in data-rich regions and have rarely considered acclimatization explicitly. The most frequent temperature (MFT) indicator provides us with the ability to solve this problem. MFT is defined as the longest period of temperature throughout the year to which a human is exposed and therefore acclimates. In this study, we propose a new method to estimate the number of heat exposure days from the perspective of temperature distribution and MFT, based on the daily mean temperature readings of 2142 weather stations in eight major climate zones in China over the past 20 years. This method can be used to calculate the number of heat exposure days in terms of heat-related mortality risk without the need for mortality data. We estimated the distribution and changes of annual mean temperature (AMT), minimum mortality temperature (MMT), and the number of heat exposure days in different climate zones in China. The AMT, MMT, and number of heat exposure days vary considerably across China. They all tend to decrease gradually from low to high latitudes. Heat exposure days are closely related to the risk of heat-related mortality. In addition, we utilized multiple linear regression (MLR) to analyze the association between the risk of heat-related mortality and the city and its climatic characteristics. Results showed that the number of heat exposure days, GDP per capita, urban population ratio, proportion of elderly population, and climate zone were found to modify the estimate on heat effect, with an R2 of 0.71. These findings will be helpful for the creation of public policies protecting against high-temperature-induced mortalities.

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Spatiotemporal Distribution of Heatwave Hazards in Chinese Mainland for the Period 1990-2019

Liu

Li³

et al. 2022

Preprint

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Heatwaves occurred frequently in summers, severely harming natural environment and human society. While a few longterm spatiotemporal heatwave studies have been conducted in China at the grid scale, their shortcomings involve discrete distribution and poor spatiotemporal continuity. We used daily data of 691 meteorological stations to obtain torridity index (TI) and heatwave index (HWI) datasets (0.01°), to evaluate the spatiotemporal distribution of heatwaves in Chinese mainland for 1990-2019. The results were as follows: (1) TI rose but with fluctuations. The largest increase occurred in North China in July. Areas with hazard levels of medium and above accounted for 22.16%, mainly in the eastern and southern provinces of China, South Tibet, East and South Xinjiang, and Chongqing. The hazard indicators in Chongqing and central Zhejiang were at especially high levels, which is concerning. (2) Average heatwave frequency, intensity, and duration reached relatively high levels of [6][7][8][20][21][22][23][24][25][11][12][13][14][15][16] respectively, in East and South Xinjiang and Southeast Tibet. (3) The study areas were divided into four categories according to the spatiotemporal distribution of hazards. The "high hazard and rapidly increasing" and "low hazard and keep increasing" areas accounted for 8.71% and 41.33%, respectively. (4) The proportions of units with significantly increased average hazard (AH) at city and county levels were 57% and 68%, respectively. Jinhua,

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Spatiotemporal Distribution and Risk Assessment of Heat Waves Based on Apparent Temperature in the One Belt and One Road Region

Cited by 24 publications

References 66 publications

Spatiotemporal Variation Analysis of the Fine-Scale Heat Wave Risk along the Jakarta-Bandung High-Speed Railway in Indonesia

Spatiotemporal Variation Analysis of the Fine-Scale Heat Wave Risk along the Jakarta-Bandung High-Speed Railway in Indonesia

A New Method to Estimate Heat Exposure Days and Its Impacts in China

Spatiotemporal Distribution of Heatwave Hazards in Chinese Mainland for the Period 1990-2019

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