Articles | Volume 19, issue 7
https://doi.org/10.5194/nhess-19-1433-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-19-1433-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jul 2019
Research article |
| 17 Jul 2019
| 17 Jul 2019
Global detection of rainfall-triggered landslide clusters
Institute of Applied
Geosciences (AGW), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
School of Global Policy
and Strategy (GPS), University of California San Diego (UCSD), La Jolla, CA, USA
Philipp Blum
CORRESPONDING AUTHOR
Institute of Applied
Geosciences (AGW), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Cited
16 citations as recorded by crossref.
- Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling M. Riaz et al. 10.1080/10106049.2022.2066202
- Analysis of extreme rainfall and landslides in the metropolitan region of the Paraiba do Sul River Valley and North Coast of Sao Paulo, Brazil R. da Silva et al. 10.1007/s00704-024-04857-2
- An approach based on socio-politically optimized neural computing network for predicting shallow landslide susceptibility at tropical areas V. Nhu et al. 10.1007/s12665-021-09525-6
- Study on the Coevolution Mechanism of Slope and Prevention Structures under Rainfall L. Tang et al. 10.1155/2022/2700084
- Application of a new basalt fiber-reinforced polymer anchorage structure in the tunnel—slope system under rainfall action L. Tang et al. 10.1007/s11629-022-7483-6
- Landslides forecasting using satellite rainfall estimations and machine learning in the Colombian Andean region D. Gómez et al. 10.1016/j.jsames.2023.104293
- Assessing the impact of governance policies on landslide risk in Brazilian municipalities V. Gonçalves Sales 10.1016/j.ijdrr.2023.104058
- Acid rain: an unsuspected factor predisposing Panzhihua airport landslide, China K. Li et al. 10.1007/s11356-021-13308-8
- A global landslide non-susceptibility map G. Jia et al. 10.1016/j.geomorph.2021.107804
- Developing a more complete understanding of tropical montane forest disturbance ecology through landslide research C. Freund & M. Silman 10.3389/ffgc.2023.1091387
- Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog C. Dandridge et al. 10.3390/su15043323
- Mapping Landslide Susceptibility Over Large Regions With Limited Data J. Woodard et al. 10.1029/2022JF006810
- Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine A. Handwerger et al. 10.5194/nhess-22-753-2022
- Homogeneous regions for rainfall distribution in the city of Rio de Janeiro associated with the risk of natural disasters R. Pereira et al. 10.1007/s11069-021-05056-2
- Landslide Hazard and Environment Risk Assessment E. Miccadei et al. 10.3390/land11030428
- Dynamic process, influence, and triggering mechanism of slope remodelling by landslide clusters in the South Jingyang Tableland, China S. Hu et al. 10.1016/j.catena.2022.106518
16 citations as recorded by crossref.
- Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling M. Riaz et al. 10.1080/10106049.2022.2066202
- Analysis of extreme rainfall and landslides in the metropolitan region of the Paraiba do Sul River Valley and North Coast of Sao Paulo, Brazil R. da Silva et al. 10.1007/s00704-024-04857-2
- An approach based on socio-politically optimized neural computing network for predicting shallow landslide susceptibility at tropical areas V. Nhu et al. 10.1007/s12665-021-09525-6
- Study on the Coevolution Mechanism of Slope and Prevention Structures under Rainfall L. Tang et al. 10.1155/2022/2700084
- Application of a new basalt fiber-reinforced polymer anchorage structure in the tunnel—slope system under rainfall action L. Tang et al. 10.1007/s11629-022-7483-6
- Landslides forecasting using satellite rainfall estimations and machine learning in the Colombian Andean region D. Gómez et al. 10.1016/j.jsames.2023.104293
- Assessing the impact of governance policies on landslide risk in Brazilian municipalities V. Gonçalves Sales 10.1016/j.ijdrr.2023.104058
- Acid rain: an unsuspected factor predisposing Panzhihua airport landslide, China K. Li et al. 10.1007/s11356-021-13308-8
- A global landslide non-susceptibility map G. Jia et al. 10.1016/j.geomorph.2021.107804
- Developing a more complete understanding of tropical montane forest disturbance ecology through landslide research C. Freund & M. Silman 10.3389/ffgc.2023.1091387
- Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog C. Dandridge et al. 10.3390/su15043323
- Mapping Landslide Susceptibility Over Large Regions With Limited Data J. Woodard et al. 10.1029/2022JF006810
- Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine A. Handwerger et al. 10.5194/nhess-22-753-2022
- Homogeneous regions for rainfall distribution in the city of Rio de Janeiro associated with the risk of natural disasters R. Pereira et al. 10.1007/s11069-021-05056-2
- Landslide Hazard and Environment Risk Assessment E. Miccadei et al. 10.3390/land11030428
- Dynamic process, influence, and triggering mechanism of slope remodelling by landslide clusters in the South Jingyang Tableland, China S. Hu et al. 10.1016/j.catena.2022.106518
Latest update: 26 Apr 2024
Short summary
This study aims to identify clusters of landslide events within a global database that are triggered by the same rainfall event. Results show that 14 % of all recorded landslide events are actually part of a landslide cluster consisting of at least 10 events. However, in a more regional analysis this number ranges from 30 % for the west coast of North America to 3 % in the Himalayan region. These findings provide an improved understanding for managing landslide mitigations on a larger scale.
This study aims to identify clusters of landslide events within a global database that are...
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