JOURNAL OF NATURAL RESOURCES ›› 2018, Vol. 33 ›› Issue (7): 1257-1269.doi: 10.31497/zrzyxb.20170526

• Resource Evaluation • Previous Articles     Next Articles

Evaluation of Drought Monitoring Indices Based onMulti-source Data in Southwest China

HE Min, SONG Li-sheng, WANG Zhan-peng, GU Qing, WANG Da-ju, GUO Bo   

  1. Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400712, China
  • Received:2017-05-29 Revised:2018-01-24 Online:2018-07-20 Published:2018-07-20
  • Supported by:
    Fundamental Research Funds for the Central Universities of China, No. XDJK2017D026, XDJK2017C004 and SWU11042

Abstract: Drought is one of the costliest natural hazards and its impacts on economic sectors and people are significant and widespread. However, drought is hard to be monitored appropriately since they are caused by the combination of anomalies in precipitation, temperature and the overall status of surface water and ground water supplies in a region. In this study, remote sensing based data, including MODIS ET, GRACE datasets, and climate datasets are used to calculate multiple drought indicators including SPI (Standardized Precipitation Index), ESI (Evaporative Stress Index), ETI (Evapotranspiration Index) and TWSC (Terrestrial Water Storage Changes). Then these drought indicators were used to track the drought events in Southwest China during 2005-2014. The results showed that all of the indicators can capture the droughts which occurred in the past decade, and the correlation coefficient between the computed SPI and ESI is the highest which is greater than 0.43 (P<0.01), compared with the correlation coefficients between other indicators. In addition, TWSC is more sensitive to widespread severe droughts and is more variable during fall and winter. However, ESI can capture the spatio-tempral distribution of the droughts in 2006, 2009 to 2010 and 2011 more accurately than SPI, ETI and TWSC, even without any input of rainfall data. Here, ESI responds to variability of soil moisture and vegetation water content which are introduced by the variations of precipitation and radiation load and is a useful complement for drought monitoring in regions where rainfall data are spare or unreliable.

Key words: drought, ESI, GRACE, MODIS, Southwest China, SPI

CLC Number: 

  • P426.616