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Evaluating the performances of satellite-based rainfall data for global rainfall-induced landslide warnings

  • Guoqiang Jia
  • Qiuhong TangEmail author
  • Ximeng Xu
Original Paper


Satellite-based precipitation estimates (SPEs) show great promise for promoting landslide warning and mitigating landslide disaster risk with quasi-global coverage, near real-time monitoring, increasing spatial-temporal resolution, and accuracy. In this study, we evaluated the performances of four SPE products in detecting the initiation of rainfall-induced landslides globally using Hanssen-Kuiper (HK) skill score based on rainfall frequentist thresholds. The results show that SPEs can distinguish rainfall events responsible for landslides from those not related to landslides, suggesting that SPEs can capture rainfall conditions corresponding to landslide occurrence well and are of great use for landslide detecting. Further investigation indicates that performances at the global scale vary with products. CMORPH-3h V1 (HK = 0.43) and TMPA-3B42RT V7 (HK = 0.42) are superior to two other rainfall products with high HK values. Rainfall threshold establishment and evaluation for specific landslide types can improve SPEs’ performances in landslide modeling with higher HK values compared to results based on all landslide records. Performances also vary spatially with HK values ranging from 0.1 to 0.9 at a spatial grid of 5° × 5°. Linear relationship analysis reveals the variation in mean annual precipitation can partially explain the heterogeneous spatial distribution of rainfall threshold parameters. These findings serve to promote the application of satellite-based rainfall data in landslide warnings.


Rainfall-induced landslides Satellite-based precipitation estimates Landslide warning Empirical rainfall thresholds Skill scores 


Funding information

This research is supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060402), the National Natural Science Foundation of China (41425002, 41730645, and 41790424), the International Partnership Program of Chinese Academy of Sciences (131A11KYSB20170113), and Newton Advanced Fellowship.


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Copyright information

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

Authors and Affiliations

  1. 1.Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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