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Risk early warning of maize drought disaster in Northwestern Liaoning Province, China

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  • Qi Zhang
  • Jiquan Zhang
  • Chunyi Wang
  • Liang Cui
  • Denghua Yan

Abstract

This study presents a methodology of risk early warning of maize drought disaster in Northwestern Liaoning Province from the viewpoints of climatology, geography, disaster science, environmental science, and so on. The study area was disaggregated into small grid cells, which has higher resolution than counties. Based on the daily meteorological data and maize yield data from 1997 to 2005, the risk early warning model was built up for drought disaster. The early warning crisis signs were considered from exogenous warning signs and endogenous warning signs. The probability of drought was taken as endogenous warnings sign, which was calculated by logistic regression model. Beside precipitation, wind speed and temperature were taken into consideration when assessing the drought. The optimal partition method was used to define the threshold of each warning grade. Take the year of 2009 as an example, this risk early warning model performed well in warning drought disasters of each maize-growing stage. Results obtained from the early warning model can guide the government to take emergency action to reduce the losses. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Qi Zhang & Jiquan Zhang & Chunyi Wang & Liang Cui & Denghua Yan, 2014. "Risk early warning of maize drought disaster in Northwestern Liaoning Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 701-710, June.
  • Handle: RePEc:spr:nathaz:v:72:y:2014:i:2:p:701-710
    DOI: 10.1007/s11069-013-1030-2
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    References listed on IDEAS

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    1. Donald Wilhite & Mark Svoboda & Michael Hayes, 2007. "Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 763-774, May.
    2. Jiquan Zhang & Norio Okada & Hirokazu Tatano & Seiji Hayakawa, 2004. "Damage Evaluation of Agro-meteorological Hazards in the Maize-Growing Region of Songliao Plain, China: Case Study of Lishu County of Jilin Province," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 31(1), pages 209-232, January.
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    Cited by:

    1. Jingpeng Guo & Kebiao Mao & Yinghui Zhao & Zhong Lu & Xiaoping Lu, 2019. "Impact of Climate on Food Security in Mainland China: A New Perspective Based on Characteristics of Major Agricultural Natural Disasters and Grain Loss," Sustainability, MDPI, vol. 11(3), pages 1-25, February.
    2. Enliang Guo & Jiquan Zhang & Yongfang Wang & Ha Si & Feng Zhang, 2016. "Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1747-1761, September.
    3. Ning Sun & Sai Tang & Ju Zhang & Jiaxin Wu & Hongwei Wang, 2022. "Food Security: 3D Dynamic Display and Early Warning Platform Construction and Security Strategy," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    4. Nadir Elagib, 2015. "Drought risk during the early growing season in Sahelian Sudan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 1549-1566, December.
    5. Enliang Guo & Jiquan Zhang & Xuehui Ren & Qi Zhang & Zhongyi Sun, 2014. "Integrated risk assessment of flood disaster based on improved set pair analysis and the variable fuzzy set theory in central Liaoning Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 947-965, November.

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