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Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions

Author

Listed:
  • Yulin Chen

    (Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China)

  • Enze Chen

    (Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China)

  • Jun Zhang

    (Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China)

  • Jingxuan Zhu

    (Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China
    Department of Civil Engineering, University of Bristol, Bristol BS8 1TR, UK)

  • Yuanyuan Xiao

    (Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China)

  • Qiang Dai

    (Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China)

Abstract

Climate change can exacerbate the occurrence of extreme precipitation events, thereby affecting both the frequency and intensity of rainfall-induced landslides. It is important to study the threat of rainfall-induced landslides under future climate conditions for the formulation of disaster prevention and mitigation policies. Due to the complexity of the climate system, there is great uncertainty in the climate variables simulated by a global climate model (GCM), which will be further propagated in landslide prediction. In this study, we investigate the spatial and temporal trends of future landslide hazards in China under climate change, using data from a multi-model ensemble of GCMs based on two scenarios, RCP4.5 and RCP8.5. The uncertainty characteristics are then estimated based on signal-to-noise ratios (SNRs) and the ratio of agreement in sign (RAS). The results show that the uncertainty of landslide prediction is mainly dominated by the GCM ensemble and the RCP scenario settings. Spatially, the uncertainty of landslide prediction is high in the western areas of China and low in the eastern areas of China. Temporally, the uncertainty of landslide prediction is evolving, with characteristics of high uncertainty in the near future and characteristics of low uncertainty in the distant future. The annual average SNRs in the 21st century are 0.44 and 0.50 in RCP4.5 and RCP8.5, respectively, and the RAS of landslide prediction in Southeastern China is only 50–60%. This indicates that more than half of the patterns show trends that are opposite to those of the ensemble, suggesting that their landslide change trends are not universally recognized in the pattern ensemble. Considering the uncertainty of climate change in landslide prediction can enable studies to provide a more comprehensive picture of the possible range of future landslide changes, effectively improving the reliability of landslide hazard prediction and disaster prevention.

Suggested Citation

  • Yulin Chen & Enze Chen & Jun Zhang & Jingxuan Zhu & Yuanyuan Xiao & Qiang Dai, 2023. "Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions," Land, MDPI, vol. 12(9), pages 1-16, September.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:9:p:1732-:d:1233986
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    References listed on IDEAS

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    1. Qigen Lin & Ying Wang & Thomas Glade & Jiahui Zhang & Yue Zhang, 2020. "Assessing the spatiotemporal impact of climate change on event rainfall characteristics influencing landslide occurrences based on multiple GCM projections in China," Climatic Change, Springer, vol. 162(2), pages 761-779, September.
    2. Thomas Stanley & Dalia B. Kirschbaum, 2017. "A heuristic approach to global landslide susceptibility mapping," 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. 87(1), pages 145-164, May.
    3. Stephen R. Sobie, 2020. "Future changes in precipitation-caused landslide frequency in British Columbia," Climatic Change, Springer, vol. 162(2), pages 465-484, September.
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