基于遥感的宁夏地区高温热浪风险评估

doi:10.12118/j.issn.1000-6060.2021.280

干旱区地理 ›› 2022, Vol. 45 ›› Issue (2): 512-521.doi: 10.12118/j.issn.1000-6060.2021.280

基于遥感的宁夏地区高温热浪风险评估

赵志欣^1,²(),霍艾迪^1,²(),张丹³,易秀^1,²,陈思名^1,²,陈四宾^1,²,陈建^1,²

1.长安大学水利与环境学院,陕西西安 710054
2.长安大学旱区地下水文与生态效应教育部重点实验室,陕西西安 710054
3.中国科学院地理科学与资源研究所,北京 100101

收稿日期:2021-06-18 修回日期:2021-09-26 出版日期:2022-03-25 发布日期:2022-04-02
通讯作者: 霍艾迪
作者简介:赵志欣（1996-）,女,硕士研究生,主要从事生态环境变化与灾害等方面的研究. E-mail: zhao1201xin@163.com
基金资助:
国家自然科学基金重大项目(41790444/D0214);国家自然科学基金面上项目(41877232);中国科学院战略性先导科技专项(A类)(XDA20030302);宁夏自然科学基金项目资助(2020AAC03476)

Assessing heat wave risk in Ningxia segment based on remote sensing

ZHAO Zhixin^1,²(),HUO Aidi^1,²(),ZHANG Dan³,YI Xiu^1,²,CHEN Siming^1,²,CHEN Sibin^1,²,CHEN Jian^1,²

1. School of Water and Environmental, Chang’an University, Xi’an 710054, Shaanxi, China
2. Key Laboratory of Subsurface Hydrology and Ecological Effects in the Arid Region Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, China
3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received:2021-06-18 Revised:2021-09-26 Online:2022-03-25 Published:2022-04-02
Contact: Aidi HUO

摘要/Abstract

摘要：

高温热浪灾害风险信息对全球气候变暖及快速城市化条件下的防控极端灾害事件具有重要的参考价值。为了解决高温热浪危险性因子评估不全面的问题,基于多源卫星遥感数据和社会经济统计数据,在结合地表温度和气象数据作为高温危险性因子的基础上,通过层次分析法和图层叠置法评估模型计算得到2014—2019年7—8月宁夏高温热浪风险等级空间分布图。结果表明：宁夏高温热浪风险总体上处于中等偏上水平,较高和高风险地区面积占比从2014年39.52%增长至2019年62.65%;受地理纬度、地形和气候的影响,高温风险分布出现明显的空间差异,北部风险总体高于南部风险（高出约13.27%）,西部风险高于东部（高出约12.30%）;高风险地区集中在中卫市和石嘴山市,这主要是城市高温和相对较低的医疗水平共同作用的结果。研究结果有助于服务城市高温灾害的预防以及制定应对高温热浪应急方案。

关键词: 高温热浪, 人居指数, 风险评估, 遥感, 宁夏

Abstract:

The disaster risk information of the heat wave is of great reference value for preventing and controlling extreme disaster events under global warming and rapid urbanization. Since the spatial distribution of population and social and economic factors are closely related to regional high temperature risk, this paper combines these two factors to the evaluation index of heat waves risk to improve the evaluation result and make the evaluation result more consistent with reality. Additionally, meteorological data were added based on the multi-source satellite remote sensing and socio-economic statistics data to solve the problem of incomplete risk assessment of heat wave. Hazard factors were obtained by combining surface temperature and meteorological data. In Matlab software, the corresponding target, criterion, and index layers are divided according to the components of heat wave risk. Pairwise comparison is conducted on the indexes. Then, a judgment matrix is constructed according to their relative importance, and a consistency test is performed. Consequently, the weight of each index is obtained. Then, the spatial overlay tool in ArcGIS 10.3 software was used to superimpose normalized indicators according to their respective weights to obtain the spatial distribution map of heat wave risk in Ningxia Province, northwest China from July to August between 2014 and 2019. The results are as follows. The overall risk of heat wave in Ningxia is at an upper-medium level, with the proportion of high and higher risk areas increasing from 39.52% in 2014 to 62.65% in 2019. There is a significant spatial difference in heat wave risk affected by geographical latitude, topography, and climate. The northern area showed higher heat risk than the southern area (about 13.27% higher), and the western area showed a slightly higher risk than the eastern area (about 12.30% higher). High-risk areas are concentrated in Zhongwei City and Shizuishan City because of the city’s high temperature and relatively low medical level. This study is essential for preventing urban disasters and formulating an emergency plan to deal with heat wave.

Key words: heat wave, human settlement index, risk assessment, remote sensing, Ningxia

赵志欣,霍艾迪,张丹,易秀,陈思名,陈四宾,陈建. 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.

ZHAO Zhixin,HUO Aidi,ZHANG Dan,YI Xiu,CHEN Siming,CHEN Sibin,CHEN Jian. Assessing heat wave risk in Ningxia segment based on remote sensing[J]. Arid Land Geography, 2022, 45(2): 512-521.

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参考文献 43

[1]	IPCC. Summary for policymakers[C]// Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2013: 1-30.
[2]	冯雷, 李旭东. 高温热浪对人类健康影响的研究进展[J]. 环境与健康杂志, 2016, 33(2):182-188.
	[ Feng Lei, Li Xudong. Effects of heat waves on human health: A review of recent study[J]. Journal of Environment and Health, 2016, 33(12):182-188. ]
[3]	张晓东, 赵银鑫, 武丹, 等. 基于遥感的银川市建成区城市扩展及其热环境变化分析[J]. 干旱区地理, 2020, 43(5):1278-1288.
	[ Zhang Xiaodong, Zhao Yinxin, Wu Dan, et al. 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. ]
[4]	Yang J, Ou C Q, Ding Y, et al. Daily temperature and mortality: A study of distributed lag non-linear effect and effect modification in Guangzhou[J]. Environmental Health, 2012, 11(1):63, doi: 10.1186/1476-069X-11-63. doi: 10.1186/1476-069X-11-63
[5]	López-Bueno J A, Navas-Martín M A, Linares C, et al. Analysis of the impact of heat waves on daily mortality in urban and rural areas in Madrid[J]. Environmental Research, 2021, 195(1):110892, doi: 110810.111016/j.envres.112021.110892. doi: 110810.111016/j.envres.112021.110892
[6]	Lansu E M, Heerwaarden C C, Stegehuis A I, et al. Atmospheric aridity and apparent soil moisture drought in european forest during heat waves[J]. Geophysical Research Letters, 2020, 47(6): e2020GL087091, doi: 87010.81029/82020GL087091. doi: 87010.81029/82020GL087091
[7]	Huo A, Peng J, Cheng Y, et al. Hydrological analysis of Loess Plateau highland control schemes in Dongzhi Plateau[J]. Frontiers in Earth Science, 2020, 8:528632, doi: 528610.523389/feart.522020.528632. doi: 528610.523389/feart.522020.528632
[8]	Lee Y Y, Grotjahn R. California central valley summer heat waves form two ways[J]. Journal of Climate, 2016, 29(3): 151208152351006, doi: 10.1175/JCLI-D-15-0270.1. doi: 10.1175/JCLI-D-15-0270.1
[9]	Hochman A, Scher S, Quinting J, et al. A new view of heat wave dynamics and predictability over the eastern Mediterranean[J]. Earth System Dynamics, 2021, 12(1):133-149. doi: 10.5194/esd-12-133-2021
[10]	Kunkel K E, Lian X Z, Zhu J H. Regional climate model projections and uncertainties of U.S. summer heat waves[J]. Journal of Climate, 2010, 23(16):4447-4458. doi: 10.1175/2010JCLI3349.1
[11]	Yin C, Yang F, Wang J L, et al. Spatiotemporal distribution and risk assessment of heat waves based on apparent temperature in the One Belt and One Road Region[J]. Remote Sensing, 2020, 12(7):1174, doi: 10.3390/rs12071174. doi: 10.3390/rs12071174
[12]	Semenza J C, Rubin C H, Falter K H, et al. Heat-related deaths during the july 1995 heat wave in Chicago[J]. The New England Journal of Medicine, 1996, 335(2):84-90. doi: 10.1056/NEJM199607113350203
[13]	Mandal R, Joseph S, Sahai A K, et al. Real time extended range prediction of heat waves over India[J]. Scientific Reports, 2019, 9(1):9008, doi: 10.1038/s41598-019-45430-6. doi: 10.1038/s41598-019-45430-6 pmid: 31227766
[14]	Phung D, Chu C, Tran D N, et al. Spatial variation of heat-related morbidity: A hierarchical Bayesian analysis in multiple districts of the Mekong Delta Region[J]. Science of the Total Environment, 2018, 637:1559-1565.
[15]	Kuglitsch F G, Toreti A, Xoplaki E, et al. Heat wave changes in the eastern Mediterranean since 1960[J]. Geophysical Research Letters, 2010, 37(4):379-384.
[16]	Inostroza L, Palme M, De la Barrera F. A heat vulnerability index: Spatial patterns of exposure, sensitivity and adaptive capacity for Santiago de Chile[J]. PloS one, 2016, 11(9):e0162464, doi: 0162410.0161371/journal.pone.0162464. doi: 0162410.0161371/journal.pone.0162464
[17]	Gertrud, Hatvani-Kovacs, Martin. et al. Assessment of heatwave impacts[J]. Procedia Engineering, 2016, 169:316-323. doi: 10.1016/j.proeng.2016.10.039
[18]	张晓艳, 刘梅先. 洞庭湖流域高温热浪风险变化特征[J]. 长江流域资源与环境, 2015, 24(10):1729-1735.
	[ Zhang Xiaoyan, Liu Meixian. Trends and risks of extreme heat events in the Dongting Lake catchment[J]. Resources and Environment in the Yangtze Basin, 2015, 24(10):1729-1735. ]
[19]	焦文慧, 张勃, 马彬, 等. 近58 a中国北方地区极端气温时空变化及影响因素分析[J]. 干旱区地理, 2020, 43(5):1220-1230.
	[ Jiao Wenhui, Zhang Bo, Ma Bin, et al. Temporal and spatial changes of extreme temperature and its influencing factors in northern China in recent 58 years[J]. Arid Land Geography, 2020, 43(5):1220-1230. ]
[20]	杨建玲, 冯建民, 闫军, 等. 宁夏高温气候特征及其大气环流异常分析[J]. 中国沙漠, 2012, 32(5):1417-1425.
	[ Yang Jianling, Feng Jianmin, Yan Jun, et al. Analysis of high temperature climatic characteristics in Ningxia and atmospheric circulation anomaly[J]. Journal of Desert Research, 2012, 32(5):1417-1425. ]
[21]	孙艺杰, 刘宪锋, 任志远, 等. 1960—2016年黄土高原干旱和热浪时空变化特征[J]. 地理科学进展, 2020, 39(4):591-601. doi: 10.18306/dlkxjz.2020.04.006
	[ Sun Yijie, Liu Xianfeng, Ren Zhiyuan, et al. Spatiotemporal changes of droughts and heatwaves on the Loess Plateau during 1960—2016[J]. Progress in Geography, 2020, 39(4):591-601. ] doi: 10.18306/dlkxjz.2020.04.006
[22]	刘学智, 李王成, 苏振娟, 等. 1962—2015年宁夏平均气温和极端气温的变化特征[J]. 干旱区研究, 2018, 35(5):1173-1180.
	[ Liu Xuezhi, Li Wangcheng, Su Zhenjuan, et al. Change of mean temperature and extreme temperature in Ningxia during the period of 1962—2015[J]. Arid Zone Research, 2018, 35(5):1173-1180. ]
[23]	杨宝玲, 杨婧, 陈彦虎, 等. 西北地区夏季极端高温异常特征及其成因[J]. 农业科学研究, 2014, 35(1):20-28.
	[ Yang Baoling, Yang Jing, Chen Yanhu, et al. The characteristics and causes of extremely high temperature events in summer in northwest China[J]. Journal of Agricultural Sciences, 2014, 35(1):20-28. ]
[24]	李志军. 宁夏气候变化及其对植被覆盖的影响[D]. 兰州: 兰州大学, 2010.
	[ Li Zhijun. Ningxia climate change and its effects on vegetation cover[D]. Lanzhou: Lanzhou University, 2010. ]
[25]	Huo A D, Yang L, Luo P P, et al. Influence of landfill and land use scenario on runoff, evapotranspiration, and sediment yield over the Chinese Loess Plateau[J]. Ecological Indicators, 2021, 121:107208,doi: 107210.101016/j.ecolind.102020.107208. doi: 107210.101016/j.ecolind.102020.107208
[26]	孙灏, 马立茹, 蔡创创, 等. 干旱区地表温度和热岛效应演变研究——以宁夏沿黄城市带为例[J]. 干旱区地理, 2020, 43(3):694-705.
	[ Sun Hao, Ma Liru, Cai Chuangchuang, et al. Evolution of surface temperature and heat island effect in arid areas: A case of city belt along the Yellow River in Ningxia[J]. Arid Land Geography, 2020, 43(3):694-705. ]
[27]	张明军, 汪宝龙, 魏军林, 等. 近50年宁夏极端气温事件的变化研究[J]. 自然灾害学报, 2012, 21(4):152-160.
	[ Zhang Mingjun, Wang Baolong, Wei Junlin, et al. Extreme event changes of air temperature in Ningxia in recent 50 years[J]. Journal of Natural Disasters, 2012, 21(4):152-160. ]
[28]	付含聪, 邓帆, 杨欢, 等. 基于遥感的长江中下游城市群高温热浪风险评估[J]. 长江流域资源与环境, 2020, 29(5):1174-1182.
	[ Fu Hancong, Deng Fan, Yang Huan, et al. Assessing heat wave risk of urban agglomeration in the middle-lower Yangtze River based on remote sensing[J]. Resources and Environment in the Yangtze Basin, 2020, 29(5):1174-1182. ]
[29]	边增淦, 王文, 江渊. 黑河流域中游地区作物种植结构的遥感提取[J]. 地球信息科学学报, 2019, 21(10):1629-1641. doi: 10.12082/dqxxkx.2019.190183
	[ Bian Zenggan, Wang Wen, Jiang Yuan. Remote sensing of cropping structure in the middle reaches of the Heihe River Basin[J]. Journal of Geo-information Science, 2019, 21(10):1629-1641. ] doi: 10.12082/dqxxkx.2019.190183
[30]	金星星, 祁新华, 陆玉麒, 等. 福建省高温热浪风险评估与空间分异研究[J]. 地球信息科学学报, 2018, 20(12):1820-1829. doi: 10.12082/dqxxkx.2018.180295.
	[ Jin Xingxing, Qi Xinhua, Lu Yuqi, et al. Evaluation and spatial differentiation of heat waves risk of Fujian Province[J]. Journal of Geo-information Science, 2018, 20(12):1820-1829. ] doi: 10.12082/dqxxkx.2018.180295.
[31]	武夕琳, 刘庆生, 刘高焕, 等. 高温热浪风险评估研究综述[J]. 地球信息科学学报, 2019, 21(7):1029-1039. doi: 10.12082/dqxxkx.2019.180544
	[ Wu Xilin, Liu Qingsheng, Liu Gaohuan, et al. Risk assessment of heat waves: A review[J]. Journal of Geo-information Science, 2019, 21(7):1029-1039. ] doi: 10.12082/dqxxkx.2019.180544
[32]	刘勇洪, 权维俊. 北京城市高温遥感指标初探与时空格局分析[J]. 气候与环境研究, 2014, 19(3):332-342.
	[ Liu Yonghong, Quan Weijun. Research on high temperature indices of Beijing City and its spatiotemporal pattern based on satellite data[J]. Climatic and Environmental Research, 2014, 19(3):332-342. ]
[33]	Lu D S, Tian H Q, Zhou G M, et al. Regional mapping of human settlements in southeastern China with multisensor remotely sensed data[J]. Remote Sensing of Environment, 2008, 112(9):3668-3679. doi: 10.1016/j.rse.2008.05.009
[34]	陈倩, 丁明军, 杨续超, 等. 长江三角洲地区高温热浪人群健康风险评价[J]. 地球信息科学学报, 2017, 19(11):1475-1484. doi: 10.3724/SP.J.1047.2017.01475
	[ Chen Qian, Ding Mingjun, Yang Xuchao, et al. Spatially explicit assessment of heat health risks using multi-source data: A case study of the Yangtze River Delta Region, China[J]. Journal of Geo-information Science, 2017, 19(11):1475-1484. ] doi: 10.3724/SP.J.1047.2017.01475
[35]	王彦芳, 边继云, 李国庆. 未来情景下高温对雄安新区产业劳动生产率的影响及应对策略[J]. 中国人口·资源与环境, 2020, 30(6):73-83.
	[ Wang Yanfang, Bian Jiyun, Li Guoqing. The impact of hot weather on regional labor productivity over Xiong’an New District under future climate scenario[J]. China Population, Resources and Environment, 2020, 30(6):73-83. ]
[36]	王梅梅, 朱志玲, 吴咏梅. 宁夏中部干旱带土地沙漠化评价[J]. 中国沙漠, 2013, 33(2):320-324.
	[ Wang Meimei, Zhu Zhiling, Wu Yongmei. Assessment on the sensitivity to aeolian desertification and lmportance of controlling aeolian desertification in the middle arid region of Ningxia[J]. Journal of Desert Research, 2013, 33(2):320-324. ]
[37]	陈林, 杨新国, 宋乃平, 等. 宁夏中部干旱带主要植物叶性状变异特征研究[J]. 草业学报, 2014, 23(1):41-49.
	[ Chen Lin, Yang Xin’guo, Song Naiping, et al. A study on variations in leaf trait of 35 plants in the arid region of middle Ningxia, China[J]. Acta Prataculturae Sinica, 2014, 23(1):41-49. ]
[38]	Wang J, Yan Z. Rapid rises in the magnitude and risk of extreme regional heat wave events in China[J]. Weather and Climate Extremes, 2021, 34(4):100379, doi: 100310.101016/j.wace.102021.100379. doi: 100310.101016/j.wace.102021.100379
[39]	张嘉仪, 钱诚. 1960—2018年中国高温热浪的线性趋势分析方法与变化趋势[J]. 气候与环境研究, 2020, 25(3):225-239.
	[ Zhang Jiayi, Qian Cheng. Linear trends in occurrence of high temperature and heat waves in China for the 1960—2018 period: Method and analysis results[J]. Climatic and Environmental Research, 2020, 25(3):225-239. ]
[40]	冯克鹏, 田军仓, 沈晖. 基于K-means聚类分区的西北地区近半个世纪气温变化特征分析[J]. 干旱区地理, 2019, 42(6):1239-1252.
	[ Feng Kepeng, Tian Juncang, Shen Hui. Temperature variation characteristics of northwest China based on K-means clustering partition in the past half century[J]. Arid Land Geography, 2019, 42(6):1239-1252. ]
[41]	刘学智, 李王成, 苏振娟, 等. 1962—2015年宁夏平均气温和极端气温的变化特征[J]. 干旱区研究, 2018, 35(5):1173-1180.
	[ Liu Xuezhi, Li Wangcheng, Su Zhenjuan, et al. Change of mean temperature and extreme temperature in Ningxia during the period of 1962—2015[J]. Arid Zone Research, 2018, 35(5):1173-1180. ]
[42]	杨春白雪, 李建华, 李佳康, 等. 宁夏生态移民新村人居环境适宜性评价[J]. 农业科学研究, 2020, 41(2):23-29.
	[ Yang Chunbaixue, Li Jianhua, Li Jiakang, et al. The nature suitability evaluation of human settlement environment in Ningxia ecomigration villages[J]. Journal of Agricultural Sciences, 2020, 41(2):23-29. ]
[43]	吴洪斌, 刘荣国, 王原, 等. 宁夏沙坡头自然保护区气候变化脆弱性评价[J]. 中国人口·资源与环境, 2016, 26(增刊2):30-34.
	[ Wu Hongbin, Liu Rongguo, Wang Yuan, et al. Climate change vulnerability assessment of Shapotou Nature Reserve in Ningxia[J]. China Population, Resources and Environment, 2016, 26(Suppl. 2):30-34. ]

数据名称	观测平台	时间分辨率/次·d^-1	空间分辨率/km	数据来源
LST	Aqua	2	1.00	https://modis.gsfc.nasa.gov/
NDVI	Aqua	2	0.25	https://modis.gsfc.nasa.gov/
DMSP/OLS夜间灯光	DMSP	1	1.00	http://ngdc.noaa.gov/eog/
DEM	Terra	2	0.08	http://www.gscloud.cn/search
气象数据	-	-	-	http://data.cma.cn
社会统计数据	-	-	-	2014、2017年和2019年宁夏统计年鉴

目标层（A）	准则层（B）	指标层（C）
高温热浪风险A	高温热浪危险性B₁（+0.46）	地表温度C₁（+0.4）
		气温C₂（+0.4）
		降水量C₃（-0.1）
		风速C₄（-0.05）
		气压C₅（-0.05）
	社会经济脆弱性B₂（+0.31）	人居指数C₆（+0.6）
		常住人口C₇（+0.24）
		14岁以下65岁以上人口占比C₈（+0.1）
		建筑施工人员C₉ （+0.06）
	高温热浪风险适应性B₃ （-0.23）	人均GDP C₁₀（-0.6）
		城镇每百户空调拥有率C₁₁（-0.18）
		城镇卫生机构床位数C₁₂（-0.11）
		城镇医疗卫生技术人员C₁₃（-0.11）

基于遥感的宁夏地区高温热浪风险评估

Assessing heat wave risk in Ningxia segment based on remote sensing

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[9]	李乐乐, 钞锦龙, 赵德一, 李浩杰, 吴林栋, 李佳骏. 1957—2019年山西省暴雨时空分布特征与暴雨灾害风险评估[J]. 干旱区地理, 2023, 46(5): 689-699.
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[14]	吴盈盈,王振亭. 河套平原土壤风蚀风险评估[J]. 干旱区地理, 2023, 46(3): 418-427.
[15]	邓甜甜, 耿广坡, 杨睿, 张保. 1980—2020年渭河流域高温热浪时空变化特征[J]. 干旱区地理, 2023, 46(2): 211-221.