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Natural Hazards

, Volume 71, Issue 2, pp 1215–1225 | Cite as

The assessment of drought relief by typhoon Saomai based on MODIS remote sensing data in Shanghai, China

  • Yuanshu Jing
  • Jian Li
  • Yongyuan Weng
  • Jing Wang
Original Paper

Abstract

Typhoons are one of the major natural hazards occurring frequently in Shanghai. The comprehensive assessment of drought relief by typhoon has become a major concern of scientists and government agencies in Shanghai, China. In this article, with the support of remote sensing data and the available data from local meteorological stations, the regional drought relief was investigated and the change of drought intensity was quantified by the typhoon “Saomai” between 5 and 8 August 2005. The precipitation anomaly calculated on the basis of recorded rainfall was adopted to analyze drought condition changes before and after the typhoon. Then, vegetation supply water index (VSWI) and normalized difference vegetation index (NDVI) were used to monitor the drought relief due to the consecutive shortage of summer rainfall. Impact of typhoon on drought was compared by VSWI before and after typhoon Saomei. The results showed that the typhoon alleviated the drought of the vegetation by more than 70 %, based on the spatial and temporal distribution of precipitation, the ground temperature, relative humidity, high temperature, NDVI from Shanghai area. The result shows that MODIS remote sensing data are a useful quantitative monitoring tool in drought relief by local typhoons. More strategies are necessary to be adopted for prevention and mitigation of meteorological disaster in Shanghai in recent years.

Keywords

Drought relief Typhoon Saomai Normalized difference vegetation index (NDVI) Precipitation Vegetation supply water index (VSWI) 

Notes

Acknowledgments

The authors would like to thank the support of the National Natural Science Foundation of China (Grant No. 41175098) and the Scientific and Technological Support Projects in Jiangsu Province (NO. BE3011840).

References

  1. Alley WM (1985) The palmer drought severity index as a measure of hydrological drought. Water Resour Bull 21:105–114CrossRefGoogle Scholar
  2. Anderson MC, Christopher H, Brian W, Agustin P, John RM, William PK (2011) Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. J Climate 24:2025–2044CrossRefGoogle Scholar
  3. Carlson TN, Gillies RR, Perry EM (1994) A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sens Rev 9:161–173CrossRefGoogle Scholar
  4. Chen SP, Song QX (1998) Study of mechanism on remote sensing information. Sci Press, BeijingGoogle Scholar
  5. Du G (2005) Research on vegetation index monitoring model based on EOS/MODIS data (Master’s degree thesis). Huazhong University of Science and Technology, Wuhan:1–59Google Scholar
  6. Goetz SJ (1997) Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. Int J Remote Sens 18(1):71–94CrossRefGoogle Scholar
  7. Gutman GG (1990) Towards monitoring drought from space. J Climate 3:282–295CrossRefGoogle Scholar
  8. Herrieksen BL, Durkin JW (1986) Growing period and drought early warning in African using satellite data. Int J Remote Sens 11:1608–1853Google Scholar
  9. Jin JL, Wei YM, Zou LL, Liu L, Fu J (2012) Risk evaluation of China’s natural disaster systems: an approach based on triangular fuzzy numbers and stochastic simulation. Nat Hazards 62:129–139CrossRefGoogle Scholar
  10. Karl T, Quinlan F, Ezell DS (1987) Drought termination and amelioration: its climatological probability. J Clim Appl Met 26:1198–1209CrossRefGoogle Scholar
  11. Liang Y, Zhang F, Han T (2007) Monitoring soil humidity by using EOS/MODIS VSWI product in Qingyang. J Arid Meteorol 25(1):44–47Google Scholar
  12. Lu YL, Xiao GJ (2001) Meteorological disasters and their prevention. China Meteorological Press, BeijingGoogle Scholar
  13. Mo WH, Wang ZH, Sun H (2006) Remote sensing monitoring of farmland drought based on vegetation supply water index. J Nanjing Ins Meteorol 29(3):396–401Google Scholar
  14. Palmer WC (1965) Meteorological drought. US department of commerce, Weather bureau research paper No. 45, US Weather, Washington, DC, USAGoogle Scholar
  15. Qi SH, Luo CF, Wang CY (2006) Pre-study on reverse air temperature from remote sensing—relationship between vegetation index, land surface temperature and air temperature. J Remote Sens Technol Appl 21(2):130–136Google Scholar
  16. Qin Q, Glulam A, Zhu L (2008) Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China. Int J Remote Sens 29:1983–1995CrossRefGoogle Scholar
  17. Sanjay KJ, Ravish K, Ajanta G, Archana S (2010) Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India. Nat Hazards 54:643–656CrossRefGoogle Scholar
  18. Sen Z (1998) Probabilistic formulation of spatio-temporal pattern. Theor Appl Climatol 61:197–206CrossRefGoogle Scholar
  19. Shi J, Cui LL (2012) Characteristics of high impact weather and meteorological disaster in Shanghai, China. Nat Hazards 60:951–969CrossRefGoogle Scholar
  20. Simon JM, Lisa G (2001) Probabilistic precipitation anomalies associated with ENSO. Bull Am Meteorol Soc 82(4):619–638CrossRefGoogle Scholar
  21. Song LC (2003) Droughts. China Meteorological Press, BeijingGoogle Scholar
  22. Song XN, Zhao YS (2004) Study on vegetation-temperature-water synthesis index using MODIS satellite data. J Geogr GeoInf Sci 20(2):13–17Google Scholar
  23. Stahl K, Demuth S (1999) Linking stream flow drought to occurrence of atmospheric circulation pattern. Hydrol Sci J 44(5):665–680CrossRefGoogle Scholar
  24. Tucker CJ, Chaudhary BJ (1987) Satellite remote sensing of drought conditions. Remote Sens Environ 23:243–251CrossRefGoogle Scholar
  25. Wang XP, Guo N (2003) Some research advances and methods on drought monitoring by remote sensing. J Arid Meteorol 21(4):76–81Google Scholar
  26. Wang MZ, Amati M, Thomallo F (2012) Understanding the vulnerability of migrants in Shanghai to typhoons. Nat Hazards 60:1189–1210CrossRefGoogle Scholar
  27. Wei J, Ma ZG (2003) Comparison of palmer drought severity index, percentage of precipitation anomaly and surface humid index. J Geogr Sci 55:17–124Google Scholar
  28. Xia H, Wu JJ, Liu YN (2005) Progress on drought monitoring by remote sensing in China. J Remote Sens Inf (1):55–58, 31Google Scholar
  29. Yang LP, Wu RN, Run WX (2007) Research of vegetation supply water index to monitoring of drought. J Arid Environ Monitor 21(4):226–225, 239Google Scholar
  30. Zhang L, Lu M, Quan R (2008) Analysis of typhoons in Shanghai. China Sci Technol Inf 18:19–21 (in Chinese)Google Scholar
  31. Zheng ZP, Qi SZ, Xu YT (2012) Questionable frequent occurrence of urban flood hazards in modern cities of China. Nat Hazards 65(1):1009–1010CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Yuanshu Jing
    • 1
  • Jian Li
    • 2
  • Yongyuan Weng
    • 2
  • Jing Wang
    • 2
  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

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