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Characterization and evaluation of MODIS-derived Drought Severity Index (DSI) for monitoring the 2009/2010 drought over southwestern China

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  • X. Zhang
  • Y. Yamaguchi

Abstract

This article investigates whether the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived global terrestrial Drought Severity Index (DSI) had the capability of detecting regional drought over subtropical southwestern China. Monthly, remotely sensed DSI data with 0.05° spatial resolution were used to characterize the extent, duration, and severity of drought from 2000 to 2010. We reported that southwestern China suffered from incipient to extreme droughts from November 2009 to March 2010 (referred to as the “drought period”). The area affected by drought occupied approximately 74 % of the total area of the study region, in which a moderate drought, severe drought, and an extreme drought accounted for 20, 12.7, and 13.2 % of the total area, respectively; particularly in March 2010, droughts of severe and extreme intensity covered the largest areas of drought, which were 16.1 and 18.6 %, respectively. Spatially, eastern Yunnan, western Guizhou, and Guangxi suffered from persistent droughts whose intensities ranged from mild to extreme during the drought period. Pearson’s correlation analyses were performed between DSI and the in situ meteorological station-based Standardized Precipitation Index (SPI) for validating the monitoring results of the DSI. The results showed that the DSI corresponded favorably with the time scales of the SPI; meanwhile, the DSI showed its highest correlation (mean: r = 0.58) with a three-month SPI. Furthermore, similar spatial patterns and temporal variations were found between the DSI and the three-month SPI, as well as the agro-meteorological drought observation data, when monitoring drought. Our analysis suggests that the DSI can be used for near-real-time drought monitoring with fine resolution across subtropical southwestern China, or other similar regions, based solely on MODIS-derived evapotranspiration/potential evapotranspiration and Normalized Difference Vegetation Index data. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • X. Zhang & Y. Yamaguchi, 2014. "Characterization and evaluation of MODIS-derived Drought Severity Index (DSI) for monitoring the 2009/2010 drought over southwestern 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(3), pages 2129-2145, December.
    • Handle: RePEc:spr:nathaz:v:74:y:2014:i:3:p:2129-2145
    DOI: 10.1007/s11069-014-1278-1
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    1. McVicar, Tim R. & Jupp, David L. B., 1998. "The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review," Agricultural Systems, Elsevier, vol. 57(3), pages 399-468, July.
    2. Kevin E. Trenberth & Aiguo Dai & Gerard van der Schrier & Philip D. Jones & Jonathan Barichivich & Keith R. Briffa & Justin Sheffield, 2014. "Global warming and changes in drought," Nature Climate Change, Nature, vol. 4(1), pages 17-22, January.
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    Cited by:

    1. Chujie Gao & Haishan Chen & Shanlei Sun & Victor Ongoma & Wenjian Hua & Hedi Ma & Bei Xu & Yang Li, 2018. "A potential predictor of multi-season droughts in Southwest China: soil moisture and its memory," 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. 91(2), pages 553-566, March.
    2. Manman Zhang & Dang Luo & Yongqiang Su, 2022. "Drought monitoring and agricultural drought loss risk assessment based on multisource information fusion," 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. 111(1), pages 775-801, March.
    3. Lu Wu & Liping Feng & Yizhuo Li & Jing Wang & Lianhai Wu, 2019. "A Yield-Related Agricultural Drought Index Reveals Spatio-Temporal Characteristics of Droughts in Southwestern China," Sustainability, MDPI, vol. 11(3), pages 1-13, January.
    4. Nazla Bushra & Robert V. Rohli & Nina S. N. Lam & Lei Zou & Rubayet Bin Mostafiz & Volodymyr Mihunov, 2019. "The relationship between the Normalized Difference Vegetation Index and drought indices in the South Central United States," 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. 96(2), pages 791-808, March.
    5. Zhang, Yu & Hao, Zengchao & Feng, Sifang & Zhang, Xuan & Xu, Yang & Hao, Fanghua, 2021. "Agricultural drought prediction in China based on drought propagation and large-scale drivers," Agricultural Water Management, Elsevier, vol. 255(C).
    6. Omolola M. Adisa & Muthoni Masinde & Joel O. Botai & Christina M. Botai, 2020. "Bibliometric Analysis of Methods and Tools for Drought Monitoring and Prediction in Africa," Sustainability, MDPI, vol. 12(16), pages 1-22, August.
    7. Yue Zhang & Kai Huang & Yajuan Yu & Tingting Hu & Jing Wei, 2015. "Impact of climate change and drought regime on water footprint of crop production: the case of Lake Dianchi Basin, 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. 79(1), pages 549-566, October.

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