地理学报 ›› 2017, Vol. 72 ›› Issue (7): 1261-1276.doi: 10.11821/dlxb201707011

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赵映慧1, 郭晶鹏1,2, 毛克彪2(), 项亚楠1, 李怡函3, 韩家琪2, 吴馁4   

  1. 1. 东北农业大学资源环境学院,哈尔滨 150030
    2. 中国农业科学院农业资源与农业区划研究所,北京 100081
    3. 中国地质大学土地科学技术学院,北京 100081
    4. 湖南科技大学资源环境与安全工程学院,湘潭 411201
  • 收稿日期:2016-12-06 修回日期:2017-03-15 出版日期:2017-08-07 发布日期:2017-08-08
  • 作者简介:

    作者简介:赵映慧(1976-), 男, 四川广元人, 博士, 副教授, 硕士生导师, 主要研究方向为城市、农村区域环境变化研究。E-mail: zhaoyhneau@163.com

  • 基金资助:

Spatio-temporal distribution of typical natural disasters and grain disaster losses in China from 1949 to 2015

Yinghui ZHAO1, Jingpeng GUO1,2, Kebiao MAO2(), Yanan XIANG1, Yihan LI3, Jiaqi HAN2, Nei WU4   

  1. 1. College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
    2. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,China
    3. School of Land Science and Technology, China University of Geosciences, Beijing 100081, China
    4. School of Resource, Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan 411100, Hunan, China
  • Received:2016-12-06 Revised:2017-03-15 Online:2017-08-07 Published:2017-08-08
  • Supported by:
    National Natural Science Foundation of China, No.41571427 Innovative Group Guide Project, No.Y2017JC33]


中国是一个自然灾害频发的国家,研究其自然灾害演变特征及粮食灾损规律,对实现中国社会经济可持续发展、解决中国粮食安全问题具有重要意义。本文先基于Python语言编程获取1949-2015年中国31省市自然灾害造成的受灾、成灾、绝收面积,构建灾害强度指数分析不同灾种的时序特征分异,利用趋势分析、ESDA方法分析不同灾种在省域空间的分布特征及冷热区;再获取1949-2015年粮食种植数据,通过粮食灾损估算模型、定义粮食灾损率、地理空间探测器,计算并检验中国粮食损失时空特征及分异性。结果表明:① 相比受灾面积曲线,本文构建的灾害程度指数能够更好揭示自然灾害时序演变特征;② 1949-2015年期间中国两大主力灾害(洪灾、旱灾)交替出现,未来5~10年以洪灾为主;③ 灾种排序旱灾>洪灾>风雹>低温>台风,其中旱灾、洪灾受灾占比过半;④ 省域不同灾种间空间趋势变化特征明显,区域受灾面积东部>西部,北部>南部,且北部灾种单一、南部多灾并发;⑤ 自然灾害受灾总和、旱灾、雹灾、低温空间上全局自相关性不显著,呈随机模式分布,洪涝、台风在空间分布上具有显著的全局自相关性,呈集聚模式;⑥ 1949-2015年灾害、灾损量、灾损率整体时序趋势呈现先升后降,2000年为临界点,空间分布具有异质性,单因子解释力度差异显著,多因子交互均呈非线性增强关系,胡焕庸线两侧冷热点分布呈两极化且其重心向北迁移。建议政府加强除旱减雹(西北)、除旱排内涝(东北)、排涝防冻(中部)、排涝预台(东南沿海)等工程技术措施;同时西北(环境恶劣)、东北(中国粮仓)应作为防灾减灾重点保护区,制定专项保护方案,以保证中国粮食丰产增收。

关键词: 自然灾害, 灾害强度指数, ESDA, 粮食灾损, q统计量, 中国


Prone to natural disasters, China badly needs a research into its spatio-temporal distribution of natural disasters and the corresponding grain loss to improve grain security and achieve sustainable development. By means of Python Programming Language and on the basis of grain production loss over Chinese 31 provinces from 1949 to 2015, this paper first constructed disaster intensity index to analyze temporal features of different natural disasters, and with trend analysis as well as ESDA to analyze spatial characteristics in different provinces. Then the paper collected crop planting data to calculate and test the spatio-temporal characteristics in grain loss through estimation model on grain loss, defining grain loss rate and geodetector. The conclusions of paper are: (1) compared with the curve of disaster-affected areas, disaster intensity index constructed in this paper could better present temporal changes of natural disasters; (2) China alternately suffered from flood and drought between 1949 and 2015 and in the coming 5 to 10 years the main suffering would be flood; (3) the ranking of natural disasters is: drought>flood>low temperature >hail> typhoon, among which, the areas affected by drought and flood occupied more than half of the total; (4) natural disasters show clear spatial characteristics and the ranking of regional areas prone to disasters is: eastern region> western region; northern region > southern region. Generally speaking, northern region is prone to only one particular natural disaster while southern region tends to suffer from several natural disasters in the meantime; (5) the sum of natural disasters, drought, hail and low temperature, with their random distribution in space, presented unclear spatial autocorrelation, while flood and typhoon, with their clustering model in space distribution, showed clear spatial autocorrelation; (6) from 1949 to 2015, the general temporal changes of disasters, grain loss amount and loss rate showed a feature that the figures would rise first, and then dropped with the critical point in 2000. Meanwhile, they had significant heterogeneity in spatial distribution, great difference in single-factor explanation power, and multi-factor interaction showed a nonlinear enhancement relation. The distribution of hot and cold spots on both sides of the Hu Line presented a polarization pattern and the gravity center of grain loss gradually moved northward. Accordingly, this paper proposes that our government should adopt different precautionary measures in different regions of China: measures against drought and hail in Northwest China; measures against drought and waterlogging in Northeast China; measures against flood and low temperature in Central China; measures against waterlogging and typhoon in coastal areas of Southeast China. And our government should show more concern to and formulate feasible protection plans for hostile-environment Northwest China and high-grain-production Northeast China so that a good harvest in grains could be guaranteed.

Key words: natural disaster, disaster intensity index, ESDA, grain disaster losses, q-statistic, China