Establishing radar-derived rainfall thresholds for a landslide early warning system: a case study in the Sichuan Basin, Southwest China.

Journal: Scientific reports
Published Date: Jul 20, 2025

Abstract

Rainfall-induced landslides often result in significant human and property losses, and reliable rainfall thresholds can effectively mitigate the hazards associated with them. However, constructing reliable rainfall thresholds in mountainous areas with sparse rain gauge stations is challenging. This study aims to establish reliable empirical rainfall thresholds for the landslide early warning systems (LEWSs) in the study area, utilizing radar-derived rainfall data processed by deep learning. Firstly, the accuracy of radar-derived rainfall data was verified based on the data with rain gauge measurements. Subsequently, utilizing frequency theory and Bayesian probability analysis methods, in conjunction with the collected landslide data and radar-derived rainfall data, various exceedance probability thresholds for rainfall-induced landslides were determined. Furthermore, the influence of cumulative effective antecedent rainfall on the initiation of landslides was investigated. The proposed threshold equations and the effect of antecedent rainfall on landslides are intended to aid in enhancing the LEWSs for this region. The findings provide valuable insights for managing rainfall-induced landslides, and can be applied to other areas with sparse rainfall data, offering a scientific basis for improved landslide prediction and risk management.

Authors

  • Pinliang Li
    State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China.
  • Qiang Xu
    University of Huddersfield, Queensgate, Huddersfield, United Kingdom . Electronic address: Q.Xu2@hud.ac.uk.
  • Jialiang Liu
    Quantitative Biomedical Research Center, Department of Health Data Sciences and Biostatistics, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Fulin Zhang
    School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China. guoqingmu@qut.edu.cn.
  • Xu Ji
    Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China. Electronic address: xuji@seu.edu.cn.
  • Dalei Peng
    State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China.
  • Chuanhao Pu
    State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China.
  • Wanlin Chen
    State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China.
  • Shuang Yuan
    State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China.
  • Chaoyang He
    University of Southern California, 3740 McClintock Ave, Los Angeles, CA, 90007, USA.

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