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Experiments to automatically monitor drought variation using simulated annealing algorithm

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Abstract

A drought is a period of a lack of precipitation in water-deficient areas, causing shortages in their water supply, whether atmospheric, surface, or ground water. Drought with long-duration and wide-area coverage often leads to serious social and economic losses. Consequently, drought monitoring and assessment have become a critical research topic in the area. There are a number of related studies on identifying drought with different types of data, but few aim at automatic drought tracking since drought regions are time variant. In this study, an automatic drought monitoring method is proposed based on drought region tracking. Firstly, drought regions are identified with drought indexes. A simulated annealing algorithm is then used to automatically track different drought regions in successive time intervals based on the area and location of different drought regions. Preliminary results of a case experiment indicate that the simulated annealing algorithm is suitable to be used in automatic monitors and able to achieve desirable tracking results. The proposed method based on the simulated annealing algorithm is effective for automatically monitoring the variation in drought characteristics such as the spatial extent.

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Acknowledgments

We are thankful to our colleagues Ming Wei and Nian Zhong, who provided their expertise that is greatly helpful on the research. We are also immensely grateful to the editors and reviewers for their comments on an earlier version of the manuscript, although any errors are our own and should not tarnish the reputations of these esteemed professionals. This research was supported by National Natural Science Foundation of China (71173116), National Natural Science Foundation of China (41305031), and Jiangsu Key Laboratory of Meteorological Observation and Information Processing (KDXS1401).

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Authors and Affiliations

  1. Research Center for Meteorology Engineering and Management, School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Hongbo Zhang

  2. Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan, 430205, China

    Nan Li & Wengang Zhang

  3. Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Nan Li

  4. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Hongbo Zhang & Xiaofang Pei

  5. Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Hongbo Zhang & Xiaofang Pei

Authors
  1. Hongbo Zhang
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  2. Nan Li
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  3. Wengang Zhang
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  4. Xiaofang Pei
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Corresponding author

Correspondence to Xiaofang Pei.

Appendix

Appendix

See Table 2.

Table 2 Variations in drought areas (in pixels) between t 1 and t 2 provided by the automatic monitoring method, and the drought areas in the next time interval (April 27, 2012, and represented by t 3)
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Zhang, H., Li, N., Zhang, W. et al. Experiments to automatically monitor drought variation using simulated annealing algorithm. Nat Hazards 84, 175–184 (2016). https://doi.org/10.1007/s11069-016-2414-x

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  • DOI: https://doi.org/10.1007/s11069-016-2414-x

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