Natural Hazards

, Volume 91, Issue 2, pp 553–566 | Cite as

A potential predictor of multi-season droughts in Southwest China: soil moisture and its memory

  • Chujie Gao
  • Haishan Chen
  • Shanlei Sun
  • Victor Ongoma
  • Wenjian Hua
  • Hedi Ma
  • Bei Xu
  • Yang Li
Original Paper


During the last decade, several high intensity and long duration droughts happened in Southwest China (SWC) and resulted in tremendous socioeconomic losses. Meanwhile, it is well known that soil moisture (SM) plays a key role in land–atmosphere interaction and weather/climate prediction and is a direct drought index. Thus, a general analysis of SM is beneficial to drought research and prediction over this region. Based on the SM data of Global Land Data Assimilation System V2.0, we examined the temporal variations in SM in SWC during 1961–2012. Results show that significant soil drying trend happened in autumn accompanied by an evident abrupt change in 1991. Moreover, SM exhibits a strong and season-dependent persistence. Particularly, the autumn SM anomaly shows the strongest memory that can be sustained to the next spring. Along with the decadal shift of SM, the memory time of autumn SM can extend from 3 months before 1991 to 6 months in recent years. We further used the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple time scales to identify the droughts in different seasons over SWC, and the inter-annual change patterns of autumn SM and SPEIs are generally in agreement with each other, which confirms that SM is suitable for indicating the droughts. Our results suggest that the autumn SM can be a potential predictor of persistent droughts over SWC, especially for those multi-season persistent drought events started in autumn.


Drought Soil moisture Memory Southwest China 



This work was jointly supported by the National Natural Science Foundation of China (41230422 and 41605042), the Natural Science Foundation of Jiangsu Province, China (BK20130047, BK20151525 and BK20160948), the Special Fund for Research in the Public Interest of China (GYHY201406020), the Natural Science Foundation for Higher Education Institutions in Jiangsu Province (16KJB170007) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). The GLDAS dataset can be downloaded at the site: The SPEI database is freely accessible online at the site:


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  • Chujie Gao
    • 1
    • 2
  • Haishan Chen
    • 1
    • 2
  • Shanlei Sun
    • 1
    • 2
  • Victor Ongoma
    • 3
  • Wenjian Hua
    • 1
    • 2
  • Hedi Ma
    • 1
    • 2
  • Bei Xu
    • 1
    • 2
  • Yang Li
    • 4
  1. 1.Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/International Joint Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)Nanjing University of Information Science and Technology (NUIST)NanjingChina
  2. 2.College of Atmospheric ScienceNUISTNanjingChina
  3. 3.Department of MeteorologySouth Eastern Kenya UniversityKituiKenya
  4. 4.Jiangsu Meteorological ObservatoryNanjingChina

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