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Application of hydrological model simulations in landslide predictions

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Abstract

The importance of soil moisture conditions in the initiation of landslides has been widely recognized. This study takes advantage of the distributed hydrological model to derive the soil wetness index. The derived soil wetness index is then used to determine soil wetness thresholds for landslide occurrences. In order to predict landslides based on alert zones, a zone threshold is introduced together with the soil wetness threshold to constitute the integrated threshold. We evaluate the prediction performance of the integrated thresholds with the use of skill scores and the receiver operating characteristic (ROC) curves. This study is carried out in a sub-region of the Emilia-Romagna region, Northern Italy. Results show that the derived soil wetness index could account for the hydrological process that is controlled by meteorological conditions and topographic properties. The proposed integrated threshold shows a better predictive capability than the rainfall threshold, demonstrating the effectiveness of applying the soil wetness index in landslide predictions. The optimal threshold is also determined by compromising the correct predictions and incorrect predictions; it is found that the optimal integrated threshold is more advantageous in reducing false alarms compared with the optimal rainfall threshold. This study highlights the potential of applying hydrological simulations in landslide prediction studies and provides a new way to make use of high-resolution data in zone-based landslide predictions.

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Acknowledgments

The authors acknowledge the Emilia-Romagna Geological Survey for providing landslides data and Arpae Emilia-Romagna organization for providing the meteorological data and flow data. The authors also acknowledge Dr. Stephen Birkinshaw from the University of Newcastle for the help with SHETRAN in this study. The first author would like to thank the China Scholarship Council for funding her study at the University of Bristol. This study is supported by 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)), National Natural Science Foundation of China (41871299), and the Fundamental Research Funds for the Central Universities of China (2016B42014).

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Authors and Affiliations

  1. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China

    Binru Zhao

  2. Department of Civil Engineering, University of Bristol, Bristol, UK

    Binru Zhao, Dawei Han & Lu Zhuo

  3. Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing, China

    Qiang Dai

  4. Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA

    Jun Zhang

  5. Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK

    Lu Zhuo

  6. Dipartimento di Scienze della Terra e Geologico-Ambientali, Università di Bologna, Bologna, Italy

    Matteo Berti

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  1. Binru Zhao
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  2. Qiang Dai
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Correspondence to Qiang Dai.

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Zhao, B., Dai, Q., Han, D. et al. Application of hydrological model simulations in landslide predictions. Landslides 17, 877–891 (2020). https://doi.org/10.1007/s10346-019-01296-3

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