Natural Hazards

, Volume 61, Issue 2, pp 785–801 | Cite as

Risk assessment to China’s agricultural drought disaster in county unit

  • Lu HaoEmail author
  • Xiaoyu Zhang
  • Shoudong Liu
Original Paper


China faces drought disaster risk under the changing climate. Risk analysis is a suitable approach in order to design ex-ante measure able to anticipate effects of drought on agricultural production. In this article, with the support of historic drought disaster data from 583 agro-meteorological observations (1991–2009), a risk analysis method based on information diffusion theory was applied to create a new drought risk analysis model, and the risk of China’s agriculture drought disaster was evaluated on higher spatial resolution of county unit. The results show that in more than three hundred counties of China, risk probability was biyearly or annually when Drought Affected Index (DAI) was over 5%. When DAI was up to 40%, more than one hundred counties were prone to drought disaster annually or once every 5 years. This showed that the impact of drought disaster on China’s agriculture, whether in frequency or intensity, was large. With the different level of DAI, China’s agricultural drought risk pattern showed variable pattern characteristics. When DAI was low, the distribution of county agricultural drought risk in China presented the East–West pattern of differentiation, and high risk mainly lied in the eastern, low risk mainly in the western. On the other hand, when DAI was high, the distribution of county risk appeared a pattern of high in center, and the north areas higher than the south, increased gradually from southwest to northeast. Drought risk presents a clear zonal differentiation that may be result from stepped topography, different precipitation and hazard-affected bodies. Spread of high value area of drought risk in northern may be related to the southeast monsoon and ecological degradation in northern Ecotone.


Information diffusion County unit Agro-meteorological data Agriculture risk 



This work was financed by Chinese Special Fund for Meteorological-Scientific Research in the Public Interest (No. GYHY201106025) and Chinese Special Fund for Scientific Research in the Public Interest (No. 2005DIB3J103). The authors are thankful to the China Meteorological Administration for their assistance in data sharing. We also thank the anonymous reviewers of an earlier version of this paper for their very helpful comments and suggestions.


  1. Atlas Compilation Committee of Geology (2002) Atlas of Geology of the People's Republic of China (in Chinese). China Geology Press, BeijingGoogle Scholar
  2. Birkmann J (2007) Risk and vulnerability indicators at different scales: applicability, usefulness and policy implications. Environ Hazards 7:20–31CrossRefGoogle Scholar
  3. Birkmann J (2010) Global disaster response and reconstruction: stabilization versus destabilization—challenges of the global disaster response to reduce vulnerability and risk following disasters. In: Doelemeyer A, Zimmer J, Tetzlaff G (eds) Risk and planet earth—natural hazards, vulnerability, integrated adaptation strategies. Schweizerbart, Germany, pp 43–55Google Scholar
  4. Cardona OD (2003) Indicators for disaster risk management. First Expert Meeting on Disaster Risk Conceptualization and Indicator Modelling, ManizalesGoogle Scholar
  5. Compilation Committee of Climatological Atlas of the People’s Republic of China (2002) Climatological Atlas of the People’s Republic of China (in Chinese). China Meteorological Press, BeijingGoogle Scholar
  6. Dilley M (2005) Setting priorities: global patterns of disaster risk. Royal Society, LondonGoogle Scholar
  7. Feng LH, Huang CF (2008) A risk assessment model of water shortage based on information diffusion technology and its application in analyzing carrying capacity of water resources. Water Resour Manag 22:621–633CrossRefGoogle Scholar
  8. Guo D, Shikui L (1999) Distribution of disasters risk level of grain yield in China. In: Shikui L (ed) Risk assessment and strategies of agriculture disasters in China. Meteorology Press, BeijingGoogle Scholar
  9. Hao L, Zhang X, Shu Z (2010) Risk assessment model to natural disaster in county unit based on information diffusion technology. Adv Mat Res 225–226:839–842Google Scholar
  10. Hong W, Wilhite DA (2004) An operational agricultural drought risk assessment model for Nebraska, USA. Nat Hazards 33:1–21CrossRefGoogle Scholar
  11. Huang CF (2005) Risk assessment of natural disaster—theory & practice. Science Press, BeijingGoogle Scholar
  12. Huang CF, Moraga C (2005) Extracting fuzzy if-then rules by using the information matrix technique. J Comput Syst Sci 70(1):26–52CrossRefGoogle Scholar
  13. Huang C, Shi Y (2002) Towards efficient fuzzy information processing—using the principle of information diffusion. Physica-Verlag (Springer), HeidelbergGoogle Scholar
  14. IPCC (2007) Climate change: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  15. Kiem AS, Franks SW (2004) Multi-decadal variability of drought risk, eastern Australia. Hydro Process 18:2039–2050CrossRefGoogle Scholar
  16. Ngigi SN, Savenije HHG, Rockstrom J, Gachen CK (2005) Hydro-economic evaluation of rainwater harvesting and management technologies: farmers investment options and risks in semi-arid Laikipia district of Kenya. Phys Chem Earth 30:772–782Google Scholar
  17. Paulo AA, Pereira LS (2007) Prediction of SPI drought class transitions using Markov chains. Water Resour Manag 21:1813–1827CrossRefGoogle Scholar
  18. Peijun S (2002) Theory on disaster science and disaster dynamics. J Nat Disasters 11(3):1–9Google Scholar
  19. Popova Z, Kercheva M (2005) CERES model application for increasing preparedness to climate variability in agricultural planning—risk analyses. Phys Chem Earth 30:117–124Google Scholar
  20. Richter GM, Semenov MA (2005) Modeling impacts of climate change on wheat yields in England and Wales: assessing drought risks. Agric Syst 84:77–97CrossRefGoogle Scholar
  21. Shikui L, Zhiguo H, Suyan W, Ronghua L, Shaoxue S, Jingluan L, Shuqing M, Changying X (2004) Risk evaluation system and models of agro-meteorological disasters. J Nat Disasters 13(1):77–87Google Scholar
  22. Solomon S, Qin D, Manning M, Marquis M, Averyt KB, Tignor MMB, Miller HL, Chen Z (eds) (2007) Climate change 2007: the physical science basis. The fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  23. Suyan W, Zhiguo H, Shikui L, Zhiguang L, Changying X (2005) Risk regionalization of winter wheat loss caused by drought in North of China. Acta Agronomica Sinica 31(3):267–274Google Scholar
  24. Wikipedia (2011) Agriculture in China. Accessed 2011
  25. Xing Z, Chungui Z, Juxin W, Hui C (2009) Risk assessment of yield losses from agro-meteorological disasters in Fujian Province. J Nat Disasters 18(1):90–94Google Scholar
  26. Yabin L, Liming L, Di X, Shaohui Z (2010) Risk assessment of flood and drought in major grain-producing areas based on information diffusion theory. Trans CSAE 26(8):1–7Google Scholar
  27. Yamoaha CF, Walters DT, Shapiro CA, Francis CA, Hayesc MJ (2000) Standardized precipitation index and nitrogen rate effects on crop yields and risk distribution in maize. Agric Ecosyst Environ 80:113–120CrossRefGoogle Scholar
  28. Yinge L (2003) Research on the drought disaster and climatic trend in the northwestern China. J Arid Land Resour Environ 17(4):113–116Google Scholar
  29. Zhang J (2004) Risk assessment of drought disaster in the maize-growing region of Songliao Plain, China. Agric Ecosyst Environ 102:133–153CrossRefGoogle Scholar
  30. Zhiguo H, Shikui L, Suyan W, Jinluan L, Changying X (2003) Study on the risk evaluation technologies of main agro-meteorological disasters and their application. J Nat Resour 18(6):692–703Google Scholar
  31. Zixi Z, Ronghua L, Wensong F (2003) Evaluation indices of drought of winter wheat in North China. J Nat Disasters 2(1):145–150Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Jiangsu Key Lab of Agricultural MeteorologyNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China
  2. 2.College of Applied MeteorologyNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China
  3. 3.Ningxia Institute of Meteorological ScienceYinchuanPeople’s Republic of China

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