Articles | Volume 22, issue 10
https://doi.org/10.5194/nhess-22-3183-2022
© Author(s) 2022. 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-22-3183-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Physically based modeling of co-seismic landslide, debris flow, and flood cascade
Bastian van den Bout
CORRESPONDING AUTHOR
Faculty of Geo-Information Science and Earth Observation (ITC),
University of Twente, Enschede, the Netherlands
Chenxiao Tang
Institute of Mountain Hazards and Environment, Chinese Academy of
Sciences & Ministry of Water Conservancy, Chengdu, China
Cees van Westen
Faculty of Geo-Information Science and Earth Observation (ITC),
University of Twente, Enschede, the Netherlands
Victor Jetten
Faculty of Geo-Information Science and Earth Observation (ITC),
University of Twente, Enschede, the Netherlands
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Cited
13 citations as recorded by crossref.
- The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection From Multisource Satellite Imagery O. Ghorbanzadeh et al. 10.1109/JSTARS.2022.3220845
- Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China L. Yu et al. 10.1016/j.gsf.2024.101802
- An integrated hydrodynamic model for runoff-generated debris flows with novel formulation of bed erosion and deposition X. Xia et al. 10.1016/j.enggeo.2023.107310
- Traditional and modified Newmark displacement methods after the 2022 Ms 6.8 Luding earthquake (Eastern Tibetan Plateau) D. Djukem et al. 10.1007/s10346-023-02194-5
- LandslideNet: A landslide semantic segmentation network based on single-temporal optical remote sensing images X. Zhu et al. 10.1016/j.asr.2024.07.038
- A hybrid framework for landslide prediction integrating machine learning and sample enhanced by spatial autocorrelation L. Ding et al. 10.1007/s00477-025-03079-w
- Numerical simulation of rainfall-induced debris flow in the Hongchun gully based on the coupling of the LHT model and the Pudasaini model H. Yin et al. 10.1007/s11069-023-05956-5
- Combination of statistical and conceptual approaches for debris-flow susceptibility modelling at a regional scale, British Columbia, Canada T. Bornaetxea et al. 10.1016/j.catena.2025.109044
- A numerical model for cascading glacier mass flow analysis (GMFA): Erosion-deposition dynamics, phase changes, and multi-hazard chain transformations R. Jiang et al. 10.1016/j.jrmge.2025.02.010
- National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. 10.1016/j.enggeo.2024.107474
- Perspective on secondary disasters: a literature review for future research K. Sahın et al. 10.1007/s10668-024-05577-3
- Multiscale Feature Modeling and Interpretability Analysis of the SHAP Method for Predicting the Lifespan of Landslide Dams Z. Huang et al. 10.3390/app15052305
- Non-Monotonic Relationships between Return Periods of Precipitation Surface Hazard Intensity B. van den Bout et al. 10.3390/w14091348
12 citations as recorded by crossref.
- The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection From Multisource Satellite Imagery O. Ghorbanzadeh et al. 10.1109/JSTARS.2022.3220845
- Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China L. Yu et al. 10.1016/j.gsf.2024.101802
- An integrated hydrodynamic model for runoff-generated debris flows with novel formulation of bed erosion and deposition X. Xia et al. 10.1016/j.enggeo.2023.107310
- Traditional and modified Newmark displacement methods after the 2022 Ms 6.8 Luding earthquake (Eastern Tibetan Plateau) D. Djukem et al. 10.1007/s10346-023-02194-5
- LandslideNet: A landslide semantic segmentation network based on single-temporal optical remote sensing images X. Zhu et al. 10.1016/j.asr.2024.07.038
- A hybrid framework for landslide prediction integrating machine learning and sample enhanced by spatial autocorrelation L. Ding et al. 10.1007/s00477-025-03079-w
- Numerical simulation of rainfall-induced debris flow in the Hongchun gully based on the coupling of the LHT model and the Pudasaini model H. Yin et al. 10.1007/s11069-023-05956-5
- Combination of statistical and conceptual approaches for debris-flow susceptibility modelling at a regional scale, British Columbia, Canada T. Bornaetxea et al. 10.1016/j.catena.2025.109044
- A numerical model for cascading glacier mass flow analysis (GMFA): Erosion-deposition dynamics, phase changes, and multi-hazard chain transformations R. Jiang et al. 10.1016/j.jrmge.2025.02.010
- National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. 10.1016/j.enggeo.2024.107474
- Perspective on secondary disasters: a literature review for future research K. Sahın et al. 10.1007/s10668-024-05577-3
- Multiscale Feature Modeling and Interpretability Analysis of the SHAP Method for Predicting the Lifespan of Landslide Dams Z. Huang et al. 10.3390/app15052305
1 citations as recorded by crossref.
Latest update: 15 Sep 2025
Short summary
Natural hazards such as earthquakes, landslides, and flooding do not always occur as stand-alone events. After the 2008 Wenchuan earthquake, a co-seismic landslide blocked a stream in Hongchun. Two years later, a debris flow breached the material, blocked the Min River, and resulted in flooding of a small town. We developed a multi-process model that captures the full cascade. Despite input and process uncertainties, probability of flooding was high due to topography and trigger intensities.
Natural hazards such as earthquakes, landslides, and flooding do not always occur as stand-alone...
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