张强,,
朱秀迪,
申泽西,
范科科,
吴子璇
北京师范大学地理科学学部，灾害风险科学研究院，100875，北京
基金项目:国家重点研发计划资助项目（2019YFA0606900）
详细信息
通讯作者:张强(1974—)，男，博士，教授. 研究方向：水文气象学研究、旱涝灾害机理及流域生态需水等研究. E-mail：zhangq68@bnu.edu.cn
中图分类号:P429计量
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被引次数:0
出版历程
收稿日期:2020-07-03
网络出版日期:2021-11-11
刊出日期:2021-10-01
Multisource data evaluation of heat risk in Shanghai
Danzhou WANG,Qiang ZHANG,,
Xiudi ZHU,
Zexi SHEN,
Keke FAN,
Zixuan WU
Academy of Disaster Risk Science, Faculty of Geographical Science, Beijing Normal University, 100875, Beijing, China
摘要
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摘要
摘要:整合遥感数据、社会经济、自然生态数据等多源数据，从危险性(H)、暴露性(E)、脆弱性(V)和适应能力(A)4个维度构建上海市高温风险(HR)评估指标体系及高温风险指数(HRI)计算模型，揭示HR等级分布特征和空间异质性规律，识别风险空间地域及其致灾类型．结果表明：1）上海市的4个维度指数均具有显著的空间集聚特征，H指数从西南到东北逐渐降低后又升高，E、V和A这3种指数都呈现“中心—外围”特征；2）上海市HR以较低和中等水平为主，呈现从西南到东北逐渐降低后又升高的特征，且同样具有显著的空间集聚性，热点区在上海市的东北部和西南部，冷点区则大范围集中在城区东部，HRI平均最高值（1.80）在中心城区的长宁区，最低值（0.55）在浦东新区；3）不同致灾类型的面积占比由大到小依次为双维度主导型（45.89%）、单维度主导型（29.32%）、三重维度主导型（13.97%）、综合主导型（8.66%），按照不同致灾类型的面积占比排序，面积最大的是H-A型（16.10%），最小的是V型（0.13%）．研究结果为高温风险灾害预报预警机制及防灾减灾措施和方案提供借鉴和启示．
关键词:高温风险/
危险性/
暴露性/
脆弱性/
适应能力/
上海市
Abstract:Impact of high temperatures on natural ecology and human life is becoming increasingly serious．How to accurately quantify and evaluate urban heat risk has become a major focus. In this study, remote sensing data, socio-economic data, natural ecological data and other multi-source data were analyzed to develop a heat risk assessment index system and heat risk evaluation model from 4 dimensions (hazard, exposure, vulnerability and adaptability)．The distribution characteristics of heat risk level and spatial heterogeneity rules were examined, risk spatial regions and disaster types across Shanghai were identified．The 4-dimensional indices showed significant spatial agglomeration in Shanghai: heat hazard index gradually decreased from southwest to northeast and then increased; the three indices of heat exposure, vulnerability and adaptability all showed "center-periphery" characteristics．The heat risk in Shanghai was found to be mainly at low and moderate levels, gradually decreasing from southwest to northeast and then increasing, with significant spatial agglomeration．Hottest spots were found in northeast and southwest of Shanghai, coldest spots were concentrated in the east．The highest value in heat risk index (HRI : 1.80) was found in Changning District in central city, the lowest value (0.55) in Pudong New District．The area proportion of different disaster causing types was: two-dimensional leading type (45.89%) > single dimensional leading type (29.32%) > triple dimensional leading type (13.97%) > comprehensive leading type (8.66%). Regarding the area proportion of different disaster causing types, the largest area was the leading type of hazard and insufficient adaptability (16.10%), the smallest was the leading type of vulnerability (0.13%)．This study provides reference for more scientific prediction and early warning of heat disaster, and for measures and schemes of disaster prevention and mitigation．
Key words:heat risk/
hazard/
exposure/
vulnerability/
adaptability/
Shanghai
