Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions
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 preventi...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/12/9/1732 |
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| author | Yulin Chen Enze Chen Jun Zhang Jingxuan Zhu Yuanyuan Xiao Qiang Dai |
| author_facet | Yulin Chen Enze Chen Jun Zhang Jingxuan Zhu Yuanyuan Xiao Qiang Dai |
| author_sort | Yulin Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:33:20Z |
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| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-10T22:33:20Z |
| publishDate | 2023-09-01 |
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| record_format | Article |
| series | Land |
| spelling | doaj.art-b9202e5477714ac29f5bb26fe4b7e5302023-11-19T11:34:08ZengMDPI AGLand2073-445X2023-09-01129173210.3390/land12091732Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate ConditionsYulin Chen0Enze Chen1Jun Zhang2Jingxuan Zhu3Yuanyuan Xiao4Qiang Dai5Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaClimate 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.https://www.mdpi.com/2073-445X/12/9/1732landslide predictionclimate changeGCMuncertaintiesLHASA model |
| spellingShingle | Yulin Chen Enze Chen Jun Zhang Jingxuan Zhu Yuanyuan Xiao Qiang Dai Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions Land landslide prediction climate change GCM uncertainties LHASA model |
| title | Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions |
| title_full | Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions |
| title_fullStr | Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions |
| title_full_unstemmed | Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions |
| title_short | Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions |
| title_sort | investigation of model uncertainty in rainfall induced landslide prediction under changing climate conditions |
| topic | landslide prediction climate change GCM uncertainties LHASA model |
| url | https://www.mdpi.com/2073-445X/12/9/1732 |
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