Acta Geographica Sinica ›› 2017, Vol. 72 ›› Issue (7): 1261-1276.doi: 10.11821/dlxb201707011

• Orginal Article • Previous Articles     Next Articles

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:2017-08-07
  • Supported by:
    National Natural Science Foundation of China, No.41571427 Innovative Group Guide Project, No.Y2017JC33]


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