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Dynamic risk assessment of drought disaster for maize based on integrating multi-sources data in the region of the northwest of Liaoning Province, China

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  • Xiaojing Liu
  • Jiquan Zhang
  • Donglai Ma
  • Yulong Bao
  • Zhijun Tong
  • Xingpeng Liu

Abstract

The traditional studies on drought disaster risk were based on the ground point data, which were unable to realize the continuity of space and the timeliness. It is shown that the monitoring and evaluation precision on drought were reduced significantly. However, remote sensing data in adequate spatial and temporal resolution can overcome these limitations. It can better monitor the crop in large area dynamically. This study presents a methodology for dynamic risk analysis and assessment of drought disaster to maize production in the northwest of Liaoning Province based on remote sensing data and GIS from the viewpoints of climatology, geography and disaster science. The model of dynamic risk assessment of drought disaster was established based on risk formation theory of natural disaster, and the expression of risk by integrating data came from sky, ground and space. The risk indexes were divided into four classes by data mining method, and the grade maps of drought disaster risk were drawn by GIS. It is shown that the spatial and temporal risk distributions of maize at each growth stage changed over time. The model has been verified against reduction in maize yield caused by drought. It demonstrated the reasonability, feasibility and reliability of the model and the methodology. The dynamic risk assessment of regional drought disaster for maize can be used as a tool, which can timely monitor the status (the possibility and extent of drought) and trends of regional drought disaster. The results obtained in this study can provide the latest information of regional drought disaster and the decision-making basis of disaster prevention and mitigation for government management and farmers. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Xiaojing Liu & Jiquan Zhang & Donglai Ma & Yulong Bao & Zhijun Tong & Xingpeng Liu, 2013. "Dynamic risk assessment of drought disaster for maize based on integrating multi-sources data in the region of the northwest of 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. 65(3), pages 1393-1409, February.
    • Handle: RePEc:spr:nathaz:v:65:y:2013:i:3:p:1393-1409
    DOI: 10.1007/s11069-012-0416-x
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    1. A. Cancelliere & G. Mauro & B. Bonaccorso & G. Rossi, 2007. "Drought forecasting using the Standardized Precipitation Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 801-819, May.
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    9. Dunxian She & Jun Xia, 2018. "Copulas-Based Drought Characteristics Analysis and Risk Assessment across the Loess Plateau of China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 547-564, January.
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    11. Jieming Chou & Tian Xian & Runze Zhao & Yuan Xu & Fan Yang & Mingyang Sun, 2019. "Drought Risk Assessment and Estimation in Vulnerable Eco-Regions of China: Under the Background of Climate Change," Sustainability, MDPI, vol. 11(16), pages 1-14, August.
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