, Volume 16, Issue 12, pp 2381–2393 | Cite as

Estimation of soil moisture using modified antecedent precipitation index with application in landslide predictions

  • Binru Zhao
  • Qiang DaiEmail author
  • Dawei Han
  • Huichao Dai
  • Jingqiao Mao
  • Lu Zhuo
  • Guiwen Rong
Original Paper


Soil moisture plays a key role in land-atmosphere interaction systems. Although it can be estimated through in situ measurements, satellite remote sensing, and hydrological modelling, using indicators to index soil moisture conditions is another useful way. In this study, one of these indicators, the antecedent precipitation index (API), is explored. Modifications were proposed to the conventional version of API by introducing two parameters to make it more in line with the physical process. First, the recession coefficient is allowed to vary with the change of air temperature, which could take into account the variation of the evapotranspiration process. Second, the API value is restricted by the maximum value of API, accounting for the maximum water holding capacity of the soil. The modified API was then calibrated and validated by comparing with the in situ measured soil moisture. The better correlation between these two datasets demonstrates that the modified API could better indicate soil moisture conditions, compared with the conventional API. The capability of the modified API to index soil moisture conditions was further explored by applying it to landslide predictions in the Emilia-Romagna region, northern Italy. Here, the recent 3-day rainfall vs the antecedent soil wetness thresholds (RS thresholds) were constructed, in which the soil wetness is indexed by the modified API. The validation of RS thresholds was carried out with the use of the contingency matrix and receiver operating characteristic (ROC) curves. By comparing the prediction performance between RS thresholds and rainfall thresholds, it is found that RS threshold could provide better prediction capabilities in terms of higher hit rate and lower false alarm rate. The positive results indicate that the modified API could provide superior performance of indexing soil moisture conditions, demonstrating the effectiveness of the proposed modifications.


Antecedent precipitation index Soil moisture  Landslide prediction 



The authors acknowledge Dr. Matteo Berti for providing landslides data and Arpae Emilia-Romagna organization for providing the meteorological data. The first author would like to thank the China Scholarship Council for funding her study at the University of Bristol.

Funding information

This study is supported by the National Natural Science Foundation of China (41871299), Resilient Economy and Society by Integrated SysTems modelling (RESIST) (Newton Fund via Natural Environment Research Council (NERC) and Economic and Social Research Council (ESRC) (NE/N012143/1)) and the Fundamental Research Funds for the Central Universities of China (2016B42014).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Binru Zhao
    • 1
    • 2
  • Qiang Dai
    • 3
    Email author
  • Dawei Han
    • 2
  • Huichao Dai
    • 1
  • Jingqiao Mao
    • 1
  • Lu Zhuo
    • 2
  • Guiwen Rong
    • 4
  1. 1.College of Water Conservancy and Hydropower EngineeringHohai UniversityNanjingChina
  2. 2.Department of Civil EngineeringUniversity of BristolBristolUK
  3. 3.Key Laboratory of VGE of Ministry of EducationNanjing Normal UniversityNanjingChina
  4. 4.College of Earth and EnvironmentAnhui University of Science and TechnologyHuainanChina

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