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A Quantitative Analysis Method of Regional Rainfall-Induced Landslide Deformation Response Variation Based on a Time-Domain Correlation Model

Author

Listed:
  • Tingchen Wu

    (Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
    Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
    Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Xiao Xie

    (Key Laboratory for Environmental Computation and Sustainability of Liaoning Province, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
    Weifang Institute of Modern Agriculture and Ecological Environment, Weifang 261041, China)

  • Haoyu Wu

    (Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Haowei Zeng

    (Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Xiaoya Zhu

    (Geotechnical Engineering & Surveying Research Institute Co., Ltd., Hangzhou 310012, China)

Abstract

Landslide deformation is the most intuitive and effective characterization of the evolution of landslides and reveals the inherent risk of landslides. Considering the inadequacy of existing deformation monitoring data for early warnings regarding landslide hazards, resulting in insufficient disaster response times, this paper proposes a time-domain correlation model. Based on the process of rainfall-induced landslide deformation, the time-domain correlation between regional rainfall and landslide deformation is proposed, which can reflect the temporal characteristics of landslide responses to rainfall, and the calculation method of the impulse response function is designed to quantitatively model and calculate the correlation. Furthermore, rainfall monitoring data are used to optimize the landslide deformation monitoring indicator system for early warnings regarding landslide instability. The feasibility of the method proposed in this paper is verified by analyzing the historical monitoring data of rainfall and landslide deformation at nine typical locations in five landslide hazard areas in Fengjie County, Chongqing city. (1) The correlation models for the XP landslide involve a delayed rainfall response time of 5 for deformation, respectively, as well as the existence of a cycle of 55–56 days, which means that the above area can advance the landslide warning by one lag time based on the cycle; (2) The correlation models for the OT landslide show consistent correlations under a 48–50-day cycle, which means that the deformation in the above areas can be predicted based on rainfall accumulation. (3) The HJWC landslide presents a turbulence correlation, which means that other monitoring data need to be supplemented and analyzed.

Suggested Citation

  • Tingchen Wu & Xiao Xie & Haoyu Wu & Haowei Zeng & Xiaoya Zhu, 2022. "A Quantitative Analysis Method of Regional Rainfall-Induced Landslide Deformation Response Variation Based on a Time-Domain Correlation Model," Land, MDPI, vol. 11(5), pages 1-19, May.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:703-:d:810821
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    References listed on IDEAS

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    1. Lingjing Li & Xin Yao & Jiaming Yao & Zhenkai Zhou & Xin Feng & Xinghong Liu, 2019. "Analysis of deformation characteristics for a reservoir landslide before and after impoundment by multiple D-InSAR observations at Jinshajiang River, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(2), pages 719-733, September.
    2. Rattana Salee & Avirut Chinkulkijniwat & Somjai Yubonchit & Suksun Horpibulsuk & Chadanit Wangfaoklang & Sirirat Soisompong, 2022. "New threshold for landslide warning in the southern part of Thailand integrates cumulative rainfall with event rainfall depth-duration," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 125-141, August.
    3. Enrico Miccadei & Cristiano Carabella & Giorgio Paglia, 2022. "Landslide Hazard and Environment Risk Assessment," Land, MDPI, vol. 11(3), pages 1-5, March.
    4. Kai Wang & Shaojie Zhang, 2021. "Rainfall-induced landslides assessment in the Fengjie County, Three-Gorge reservoir area, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 451-478, August.
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    Cited by:

    1. Yu Cao & Liyan Huang & Nur Mardhiyah Aziz & Syahrul Nizam Kamaruzzaman, 2022. "Building Information Modelling (BIM) Capabilities in the Design and Planning of Rural Settlements in China: A Systematic Review," Land, MDPI, vol. 11(10), pages 1-34, October.

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