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Spatio-temporal analysis and simulation on shallow rainfall-induced landslides in China using landslide susceptibility dynamics and rainfall I-D thresholds

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Abstract

An empirical simulation method to simulate the possible position of shallow rainfall-induced landslides in China has been developed. This study shows that such a simulation may be operated in real-time to highlight those areas that are highly prone to rainfall-induced landslides on the basis of the landslide susceptibility index and the rainfall intensity-duration (I-D) thresholds. First, the study on landslide susceptibility in China is introduced. The entire territory has been classified into five categories, among which high-susceptibility regions (Zone 4- ‘High’ and 5-‘Very high’) account for 4.15% of the total extension of China. Second, rainfall is considered as an external triggering factor that may induce landslide initiation. Real-time satellite-based TMPA 3B42 products may provide real rainfall spatial and temporal patterns, which may be used to derive rainfall duration time and intensity. By using a historical record of 60 significant past landslides, the rainfall I-D equation has been calibrated. The rainfall duration time that may trigger a landslide has resulted between 3 hours and 45 hours. The combination of these two aspects can be exploited to simulate the spatiotemporal distribution of rainfall-induced landslide hazards when rainfall events exceed the rainfall I-D thresholds, where the susceptibility category is ‘high’ or ‘very high’. This study shows a useful tool to be part of a systematic landslide simulation methodology, potentially providing useful information for a theoretical basis and practical guide for landslide prediction and mitigation throughout China.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 41501458), China Postdoctoral Science Foundation Funded Project (Grant No. 2016M592860), National Basic Research Program of China (Grant No. 2013CB733204), Key Laboratory of Mining Spatial Information Technology of NASMG (Grant Nos. KLM201309), Science Program of Shanghai Normal University (Grant No. SK201525), the Shanghai Gaofeng & Gaoyuan Project for University Academic Program Development (Grant Nos. 2013LASW-A09 & SKHL1310) and the Center of Spatial Information Science and Sustainable Development Applications, Tongji University, Shanghai, China.

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Authors and Affiliations

  1. Institute of Urban Study, Shanghai Normal University, Shanghai, 200234, China

    WeiYue Li & GuoDong Cheng

  2. Cold and Arid Region Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 730000, China

    WeiYue Li & GuoDong Cheng

  3. College of Surveying and Geo-Informatics, Tongji University, Shanghai, 200234, China

    Chun Liu & Marco Scaioni

  4. Department of Architecture, Built Environment and Construction Engineering, Politecnico Milano, Milano, 20133, Italy

    Marco Scaioni

  5. College of Architectural Engineering, Civil Engineering and Environment, Ningbo University, Ningbo, 315211, China

    WeiWei Sun

  6. Shandong Academy of Building Research, **an, 250031, China

    Yu Chen

  7. Department of Geography, Shanghai Normal University, Shanghai, 200234, China

    Dong**g Yao

  8. School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, 510275, China

    Sheng Chen

  9. Key Laboratory of Beibu Gulf Environmental Evolution and Resources Utilization, Guangxi Teachers Education University, Nanning, 530001, China

    Sheng Chen

  10. School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, 73074, USA

    Yang Hong

  11. Department of Hydraulic Engineering, Tsinghua University, Bei**g, 100084, China

    Yang Hong

  12. Institute of Tourism, Shanghai Normal University, Shanghai, 200234, China

    KaiHang Zhang

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  1. WeiYue Li
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  2. Chun Liu
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  5. Yu Chen
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  6. Dong**g Yao
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  9. KaiHang Zhang
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Correspondence to WeiYue Li or KaiHang Zhang.

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Li, W., Liu, C., Scaioni, M. et al. Spatio-temporal analysis and simulation on shallow rainfall-induced landslides in China using landslide susceptibility dynamics and rainfall I-D thresholds. Sci. China Earth Sci. 60, 720–732 (2017). https://doi.org/10.1007/s11430-016-9008-4

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