关中地区高温脆弱性评估及其时空变化研究

doi:10.12118/j.issn.1000-6060.2023.691

Abstract

Abstract:

With global warming, extreme heat events have occurred more frequently, posing significant threats to human health, and social and economic development in many regions. This study comprehensively utilizes remote sensing data and socio-economic statistics to develop a heat vulnerability evaluation index system based on the “exposure-sensitivity-adaptability” framework. This approach quantifies heat vulnerability and reveals its spatiotemporal characteristics in the Guanzhong region of Shaanxi Province, China, from 2005 to 2020. The results indicate that: (1) The high-temperature zones of summer land surface temperature in the Guanzhong region expanded from 2005 to 2020. The spatial pattern of land surface temperature remained relatively consistent, with sub-high-temperature and high-temperature zones distributed sporadically in the central area, while sub-low-temperature and low-temperature zones were concentrated in the southern Qinling Mountains. (2) Heat vulnerability in the Guanzhong region exhibited significant spatial clustering, mirroring the distribution pattern of surface temperature. Higher vulnerability zones were predominantly located in the central plains, whereas lower and low vulnerability zones were mainly found in the southern Qinling Mountains. (3) The heat vulnerability index of the Guanzhong region showed a decreasing trend from 2005 to 2020, with the proportion of higher and high vulnerability areas declining from 48.20% in 2005 to 37.49% in 2020. (4) From 2005 to 2010, changes in heat vulnerability levels were relatively minor, whereas from 2010 to 2020, the extent of changes increased substantially, with most regions experiencing a decrease in vulnerability levels. This change was primarily characterized by medium vulnerability shifting to lower vulnerability, higher vulnerability reducing to medium vulnerability, and high vulnerability decreasing to higher vulnerability. The findings offer valuable insights for enhancing heat adaptability and mitigating heat vulnerability.

Key words: land surface temperature, heat vulnerability, spatial-temporal variation, Guanzhong area

BAO Wei, HUANG Xiaojun, JI Wangdi. Evaluation of heat vulnerability and its spatial-temporal variation in the Guanzhong area[J].Arid Land Geography, 2024, 47(11): 1863-1875.

Figures/Tables 12

Fig. 1

Tab. 1

Tab. 2

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

References 38

[1]	IPCC. Climate change 2021:The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change[R]. Cambridge: Cambridge University Press, 2021.
[2]	Whitman S, Good G, Donoghue E R, et al. Mortality in Chicago attributed to the July 1995 heat wave[J]. American Journal of Public Health, 1997, 87(9): 1515-1518. pmid: 9314806
[3]	Stott P A, Stone D A, Allen M R. Human contribution to the European heatwave of 2003[J]. Nature, 2004, 432(7017): 610-614.
[4]	Philip S Y, Kew S, van Oldenborgh G J, et al. Rapid attribution analysis of the extraordinary heat wave on the Pacific coast of the US and Canada in June 2021[J]. Earth System Dynamics, 2022, 13(4): 1689-1713.
[5]	Borg M A, Xiang J, Anikeeva O, et al. Occupational heat stress and economic burden: A review of global evidence[J]. Environmental Research, 2021, 195: 110781, doi: 10.1016/j.envres.2021.110781.
[6]	黄大鹏, 张蕾, 高歌. 未来情景下中国高温的人口暴露度变化及影响因素研究[J]. 地理学报, 2016, 71(7): 1189-1200. doi: 10.11821/dlxb201607008
	[Huang Dapeng, Zhang Lei, Gao Ge. Changes in population exposure to high temperature under a future scenario in China and its influencing factors[J]. Acta Geographica Sinica, 2016, 71(7): 1189-1200.] doi: 10.11821/dlxb201607008
[7]	夏智宏, 刘敏, 秦鹏程, 等. 2022年长江流域高温干旱过程及其影响评估[J]. 人民长江, 2023, 54(2): 21-28.
	[Xia Zhihong, Liu Min, Qin Pengcheng, et al. Development process of high temperature and drought events over Yangtze River Basin in 2022 and assessment on its influences[J]. Yangtze River, 2023, 54(2): 21-28.]
[8]	IPCC. Climate change 2007:Impacts, adaptation and vulnerability. Contribution of working group Ⅱ to the fourth assessment report of the intergovernmental panel on climate change[R]. Cambridge: Cambridge University Press, 2007.
[9]	Frazier T G, Thompson C M, Dezzani R J. A framework for the development of the SERV model: A spatially explicit resilience-vulnerability model[J]. Applied Geography, 2014, 51: 158-172.
[10]	Hajat S, Armstrong B, Baccini M, et al. Impact of high temperatures on mortality: Is there an added heat wave effect[J]. Epidemiology, 2006, 17(6): 632-638.
[11]	谢盼, 王仰麟, 彭建, 等. 基于居民健康的城市高温热浪灾害脆弱性评价——研究进展与框架[J]. 地理科学进展, 2015, 34(2): 165-174. doi: 10.11820/dlkxjz.2015.02.005
	[Xie Pan, Wang Yanglin, Peng Jian, et al. Health related urban heat wave vulnerability assessment: Research progress and framework[J]. Progress in Geography, 2015, 34(2): 165-174.] doi: 10.11820/dlkxjz.2015.02.005
[12]	Rinner C, Taranu J P. Map-based exploratory evaluation of non-medical determinants of population health[J]. Transactions in GIS, 2006, 10(4): 633-649.
[13]	Johnson D P, Stanforth A, Lulla V, et al. Developing an applied extreme heat vulnerability index utilizing socioeconomic and environmental data[J]. Applied Geography, 2012, 35(1): 23-31.
[14]	Aubrecht C, Özceylan D. Identification of heat risk patterns in the US National Capital Region by integrating heat stress and related vulnerability[J]. Environment International, 2013, 56: 65-77. doi: 10.1016/j.envint.2013.03.005 pmid: 23603733
[15]	Reid C E, O’ Neill M S, Gronlund C J, et al. Mapping community determinants of heat vulnerability[J]. Environmental Health Perspectives, 2009, 117(11): 1730-1736. doi: 10.1289/ehp.0900683 pmid: 20049125
[16]	Mushore D T, Mutanga O, Odindi J, et al. Determining extreme heat vulnerability of Harare Metropolitan City using multispectral remote sensing and socio-economic data[J]. Journal of Spatial Science, 2017, 63(1): 173-191.
[17]	Tran D N, Doan V Q, Nguyen V T, et al. Spatial patterns of health vulnerability to heat waves in Vietnam[J]. International Journal of Biometeorology, 2020, 64(7): 863-872.
[18]	叶殿秀, 尹继福, 陈正洪, 等. 1961—2010年我国夏季高温热浪的时空变化特征[J]. 气候变化研究进展, 2013, 9(1): 15-20.
	[Ye Dianxiu, Yin Jifu, Chen Zhenghong, et al. Spatiotemporal change characteristics of summer heatwaves in China in 1961—2010[J]. Climate Change Research, 2013, 9(1): 15-20.]
[19]	贾佳, 胡泽勇. 中国不同等级高温热浪的时空分布特征及趋势[J]. 地球科学进展, 2017, 32(5): 546-559. doi: 10.11867/j.issn.1001-8166.2017.05.0546
	[Jia Jia, Hu Zeyong. Spatial and temporal features and trend of different level heat waves over China[J]. Advances in Earth Science, 2017, 32(5): 546-559.] doi: 10.11867/j.issn.1001-8166.2017.05.0546
[20]	卜凡蕊, 孙鹏, 姚蕊, 等. 淮河流域高温热浪时空演变规律及成因分析[J]. 地理科学, 2021, 41(4): 705-716. doi: 10.13249/j.cnki.sgs.2021.04.017
	[Bu Fanrui, Sun Peng, Yao Rui, et al. High temperature heat waves in the Huaihe River Basin and relation to the Madden-Julian oscillation: Spatio-temporal properties and causes[J]. Scientia Geographica Sinica, 2021, 41(4): 705-716.] doi: 10.13249/j.cnki.sgs.2021.04.017
[21]	谢盼, 王仰麟, 刘焱序, 等. 基于社会脆弱性的中国高温灾害人群健康风险评价[J]. 地理学报, 2015, 70(7): 1041-1051. doi: 10.11821/dlxb201507002
	[Xie Pan, Wang Yanglin, Liu Yanxu, et al. Incorporating social vulnerability to assess population health risk due to heat stress in China[J]. Acta Geographica Sinica, 2015, 70(7): 1041-1051.] doi: 10.11821/dlxb201507002
[22]	郑雪梅, 王怡, 吴小影, 等. 近20年福建省沿海与内陆城市高温热浪脆弱性比较[J]. 地理科学进展, 2016, 35(10): 1197-1205. doi: 10.18306/dlkxjz.2016.10.003
	[Zheng Xuemei, Wang Yi, Wu Xiaoying, et al. Comparison of heat wave vulnerability between coastal and inland cities of Fujian Province in the past 20 years[J]. Progress in Geography, 2016, 35(10): 1197-1205.] doi: 10.18306/dlkxjz.2016.10.003
[23]	陈恺, 唐燕. 城市高温热浪脆弱性空间识别与规划策略应对——以北京中心城区为例[J]. 城市规划, 2019, 43(12): 37-44.
	[Chen Kai, Tang Yan. Identification of urban areas vulnerability to heat waves and coping strategies: A case study of Beijing central city[J]. City Planning Review, 2019, 43(12): 37-44.]
[24]	黄晓军, 祁明月, 赵凯旭, 等. 高温影响下西安市人口脆弱性评估及其空间分异[J]. 地理研究, 2021, 40(6): 1684-1700. doi: 10.11821/dlyj020200922
	[Huang Xiaojun, Qi Mingyue, Zhao Kaixu, et al. Assessment of population vulnerability to heat stress and spatial differentiation in Xi’an[J]. Geographical Research, 2021, 40(6): 1684-1700.]
[25]	Walter L F, Leyre E I, Alice N, et al. Coping with the impacts of urban heat islands: A literature based study on understanding urban heat vulnerability and the need for resilience in cities in a global climate change context[J]. Journal of Cleaner Production, 2018, 171: 1140-1149.
[26]	杨林川, 杨皓森, 范强雪, 等. 大城市高温热浪脆弱性评价及规划应对研究——以成都市为例[J]. 规划师, 2023, 39(2): 38-45.
	[Yang Linchuan, Yang Haosen, Fan Qiangxue, et al. Vulnerability assessment and planning response to high-temperature wave in large cities: The case of Chengdu[J]. Planners, 2023, 39(2): 38-45.]
[27]	Azevedo J A, Chapman L, Muller C L. Quantifying the daytime and night-time urban heat island in Birmingham, UK: A comparison of satellite derived land surface temperature and high resolution air temperature observations[J]. Remote Sensing, 2016, 8(2): 153-170.
[28]	Cao J, Zhou W Q, Zheng Z, et al. Within-city spatial and temporal heterogeneity of air temperature and its relationship with land surface temperature[J]. Landscape and Urban Planning, 2021, 206: 103979, doi: 10.1016/j.landurbplan.2020.103979.
[29]	Kovats R S, Hajat S. Heat stress and public health: A critical review[J]. Annual Review of Public Health, 2008, 29: 41-55. pmid: 18031221
[30]	郭禹慧, 黄晓军, 郑殿元, 等. 极端高温胁迫下中国城市脆弱性格局与影响因素[J]. 热带地理, 2021, 41(3): 596-608. doi: 10.13284/j.cnki.rddl.003341
	[Guo Yuhui, Huang Xiaojun, Zheng Dianyuan, et al. Urban vulnerability pattern and influencing factors under extreme heat stress in China[J]. Tropical Geography, 2021, 41(3): 596-608.] doi: 10.13284/j.cnki.rddl.003341
[31]	赵志欣, 霍艾迪, 张丹, 等. 基于遥感的宁夏地区高温热浪风险评估[J]. 干旱区地理, 2022, 45(2): 512-521. doi: 10.12118/j.issn.1000-6060.2021.280
	[Zhao Zhixin, Huo Aidi, Zhang Dan, et al. Assessing heat wave risk in Ningxia segment based on remote sensing[J]. Arid Land Geography, 2022, 45(2): 512-521.] doi: 10.12118/j.issn.1000-6060.2021.280
[32]	博文静, 沈钰仟, 王馨悦, 等. 植被覆盖度对夏季降温效应的影响——以内蒙古为例[J]. 生态学报, 2022, 42(22): 9165-9174.
	[Bo Wenjing, Shen Yuqian, Wang Xinyue, et al. The influence of vegetation coverage on cooling effect in summer: A case study of the Inner Mongolia[J]. Acta Ecologica Sinica, 2022, 42(22): 9165-9174.]
[33]	Wan Z M. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product[J]. Remote Sensing of Environment, 2014, 140: 36-45.
[34]	张秀, 王旭红, 郑玉蓉, 等. 气溶胶光学厚度和不透水地表覆盖度对城市热岛强度的影响——以关中地区为例[J]. 生态学报, 2021, 41(22): 8965-8976.
	[Zhang Xiu, Wang Xuhong, Zheng Yurong, et al. Effects of aerosol optical depth and impervious surface percentage on urban heat island intensity: A case study in Guanzhong Region[J]. Acta Ecologica Sinica, 2021, 41(22): 8965-8976.]
[35]	李菲菲, 周霞, 周玉玺. 西北地区农业干旱脆弱性评估及时空分布特征[J]. 干旱区研究, 2023, 40(4): 663-669. doi: 10.13866/j.azr.2023.04.15
	[Li Feifei, Zhou Xia, Zhou Yuxi. Vulnerability assessment and spatiotemporal distribution of agricultural drought in northwest China[J]. Arid Zone Research, 2023, 40(4): 663-669.] doi: 10.13866/j.azr.2023.04.15
[36]	黄晓军, 王博, 刘萌萌, 等. 中国城市高温特征及社会脆弱性评价[J]. 地理研究, 2020, 39(7): 1534-1547. doi: 10.11821/dlyj020190608
	[Huang Xiaojun, Wang Bo, Liu Mengmeng, et al. Characteristics of urban extreme heat and assessment of social vulnerability in China[J]. Geographical Research, 2020, 39(7): 1534-1547.]
[37]	梁秀娟, 王旭红, 牛林芝, 等. 大西安都市圈城市热岛效应时空分布特征及AOD对热岛强度的影响研究[J]. 生态环境学报, 2020, 29(8): 1566-1580. doi: 10.16258/j.cnki.1674-5906.2020.08.008
	[Liang Xiujuan, Wang Xuhong, Niu Linzhi, et al. Research on the temporal and spatial distribution characteristics of urban heat island effect and the influence of AOD on urban heat island intensity in the greater Xi’an metropolitan area[J]. Ecology and Environmental Sciences, 2020, 29(8): 1566-1580.]
[38]	王丹舟, 张强, 朱秀迪, 等. 基于多源数据的上海市高温热浪风险评估[J]. 北京师范大学学报(自然科学版), 2021, 57(5): 613-623.
	[Wang Danzhou, Zhang Qiang, Zhu Xiudi, et al. Multisource data evaluation of heat risk in Shanghai[J]. Journal of Beijing Normal University (Natural Science Edtion), 2021, 57(5): 613-623.]

目标层	维度层	指标层	指标释义	指标权重	指标属性
高温脆弱性	暴露度	地表温度/℃	夏季地表温度均值	1.0000	正
	敏感性	人口密度/人·km^-2	反映研究区域人口聚集程度	0.3418	正
		5岁以下人口密度/人·km^-2	反映儿童的敏感性特征	0.3153	正
		65岁及以上人口密度/人·km^-2	反映老年人的敏感性特征	0.3429	正
	适应能力	人均GDP/元	反映研究区域经济水平	0.3182	负
		每万人病床数/张·(10⁴人)^-1	反映应对高温灾害健康保障水平	0.2730	负
		每万人卫生技术人员数/人·(10⁴人)^-1	反映应对高温灾害健康保障水平	0.3314	负
		归一化植被指数（NDVI）	反映研究区域应对高温的绿化设施水平	0.0774	负

地表温度等级	地表温度范围
低温区	LST<μ-SD
次低温区	μ-SD≤LST≤μ-0.5SD
中温区	μ-0.5SD<LST≤μ+0.5SD
次高温区	μ+0.5SD<LST≤μ+SD
高温区	LST>μ+SD

Evaluation of heat vulnerability and its spatial-temporal variation in the Guanzhong area

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 12

References 38

Related Articles 15

Metrics

Comments

Recommended 0

[1]	JI Wangdi, HUANG Xiaojun, BAO Wei, MA Yaozhuang. Spatiotemporal correlation characteristics and driving forces of human activity intensity and surface temperature in the Guanzhong area [J]. Arid Land Geography, 2024, 47(6): 967-979.
[2]	QIN Gexia, MENG Zhiyuan, LI Ni. Dynamics of water use efficiency and its response to drought and land surface temperature on the Loess Plateau [J]. Arid Land Geography, 2024, 47(11): 1887-1898.
[3]	KANG Ligang, CAO Shengkui, CAO Guangchao, YAN Li, CHEN Lianxuan, LI Wenbin, ZHAO Haoran. Spatiotemporal variation of land surface temperature in Qinghai Lake Basin [J]. Arid Land Geography, 2023, 46(7): 1084-1097.
[4]	MA Li’na, ZHANG Feiyun, ZHAI Yuxin, TENG Lun, KANG Jianguo. Temporal and spatial evolution of ecosystem service value under land use change in Xinjiang from 1980 to 2020 [J]. Arid Land Geography, 2023, 46(2): 253-263.
[5]	ZHANG Zhigao, XU Xiaoman, GUO Chaofan, CAI Maotang, YUAN Zheng, ZHANG Mingzhe. Spatial and temporal characteristics and influencing factors of frost date in the Yellow River Basin from 1960 to 2020 [J]. Arid Land Geography, 2022, 45(6): 1685-1694.
[6]	WANG Jinjie,DING Jianli,ZHANG Zipeng. Change of ecological environment in Turpan and Hami cities based on remote sensing ecology index [J]. Arid Land Geography, 2022, 45(5): 1591-1603.
[7]	ZHANG Bo,LI Xuemei,QIN Qiyong,LI Chao,SUN Tianyao. Heterogeneity of the vertical distribution of snow cover in Chinese Tianshan Mountains [J]. Arid Land Geography, 2022, 45(3): 754-762.
[8]	CHENG Danni,WANG Yingqi,CHENG Yongxiang,HUANG Jingfeng. Vegetation-water vapor-land surface temperature correlation analysis of typical deserts and oases in Xinjiang [J]. Arid Land Geography, 2022, 45(2): 456-466.
[9]	ZHANG Xiao-dong, ZHAO Yin-xin, WU Dan, CHU Xiao-dong, WU Wen-zhong, ZHANG Yong, LIU Nai-jing, LI Yan. Urban built-up area expansion and thermal environment variation in Yinchuan City based on remote sensing [J]. Arid Land Geography, 2020, 43(5): 1278-1288.
[10]	JING Yue, SUN Yan-ling, GAO Shuang, CHEN Li, PAN Long, MA Han. Spatiotemporal variations of AOD and geographical detection of its influence factors in Beijing-Tianjin-Hebei region [J]. Arid Land Geography, 2020, 43(1): 87-98.
[11]	ZHAO Ming-wei, WANG Ni, SHI Hui-hui, JIANG Ling, WANG Chun. Spatial-temporal variation and its driving forces of vegetation coverage in China from 2001 to 2015 [J]. Arid Land Geography, 2019, 42(2): 324-331.
[12]	WANG Chen-guang, DUAN Si-bo, ZHANG Xiao-yu, LENG Pei, GAO Mao-fang, LI Zhao-liang. Method for retrieval of land surface temperature from NPP-VIIRS thermal infrared data [J]. , 2017, 40(6): 1264-1273.
[13]	LI Jing, HUANG Kang-gang. Characteristics of urban heat island based on MODIS daily land surface temperature product in Lanzhou [J]. , 2017, 40(6): 1235-1240.
[14]	HU Lin, WANG Qi, ZHANG Wen-jing, CHEN Jian-wen, CAO Hong li. Variation of low visibility events in Guanzhong of Shaanxi [J]. , 2016, 39(1): 41-46.
[15]	HE Jian-cun, BAI Yun-gang, ZHANG Yan-jun. Soil drought characteristics in Xinjiang with remote sensing data [J]. , 2015, 38(4): 735-742.