Articles | Volume 14, issue 4
https://doi.org/10.5194/gmd-14-1841-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1841-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Towards a model for structured mass movements: the OpenLISEM hazard model 2.0a
Bastian van den Bout
CORRESPONDING AUTHOR
Faculty of Geo-Information Science and Earth
Observation, University of Twente, Enschede, the Netherlands
Theo van Asch
State Key Laboratory of Geohazard
Prevention and Geo-Environment Protection, Chengdu University of Technology, Chengdu, China
Wei Hu
State Key Laboratory of Geohazard
Prevention and Geo-Environment Protection, Chengdu University of Technology, Chengdu, China
Chenxiao X. Tang
Institute of Mountain Hazards and Environment, Chinese Academy of
Sciences, Chengdu, China
Olga Mavrouli
Faculty of Geo-Information Science and Earth
Observation, University of Twente, Enschede, the Netherlands
Victor G. Jetten
Faculty of Geo-Information Science and Earth
Observation, University of Twente, Enschede, the Netherlands
Cees J. van Westen
Faculty of Geo-Information Science and Earth
Observation, University of Twente, Enschede, the Netherlands
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Cited
14 citations as recorded by crossref.
- Numerical Modelling of Debris Flows for Simulation-Based Decision Support: An Indian Perspective M. Abraham et al. https://doi.org/10.1007/s40098-024-00988-5
- An overview of debris-flow mathematical modelling M. Trujillo-Vela et al. https://doi.org/10.1016/j.earscirev.2022.104135
- Addressing current and future challenges in the engineering geology community - A young engineering geologist’s perspective E. Karantanellis et al. https://doi.org/10.1007/s10064-026-04847-w
- Sensitivity and Calibration of Three‐Dimensional SPH Formulations in Large‐Scale Landslide Modeling S. Li et al. https://doi.org/10.1029/2022JB024583
- On the estimation of landslide intensity, hazard and density via data-driven models M. Di Napoli et al. https://doi.org/10.1007/s11069-023-06153-0
- Mechanisms and river blocking effects of clustering debris flows on 20 August 2019 along the Minjiang River, Wenchuan, China Y. Luo et al. https://doi.org/10.1007/s12040-024-02502-0
- r.avaflow v4, a multi-purpose landslide simulation framework M. Mergili et al. https://doi.org/10.5194/gmd-18-9879-2025
- An integrated framework for wildfire emergency response and post-fire debris flow prediction: a case study from the wildfire event on 20 April 2021 in Mianning, Sichuan, China Y. Tang et al. https://doi.org/10.1007/s11069-025-07270-8
- Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al. https://doi.org/10.5194/nhess-24-823-2024
- The impact of terrain model source and resolution on snow avalanche modeling A. Miller et al. https://doi.org/10.5194/nhess-22-2673-2022
- Short to long term space-time prediction of rain-induced landslides under uncertainty A. Mondini et al. https://doi.org/10.1016/j.scitotenv.2025.179453
- Debris flow risk assessment via numerical simulation: a case study in Northeast China L. Wei et al. https://doi.org/10.1007/s10346-025-02545-4
- A Hybrid Theory-Driven and Data-Driven Modeling Method for Solving the Shallow Water Equations S. Yao et al. https://doi.org/10.3390/w15173140
- A deterministic two-phase model for an active suspension with non-spherical active particles using the Eulerian spatial averaging theory B. Deußen et al. https://doi.org/10.1063/5.0077735
14 citations as recorded by crossref.
- Numerical Modelling of Debris Flows for Simulation-Based Decision Support: An Indian Perspective M. Abraham et al. https://doi.org/10.1007/s40098-024-00988-5
- An overview of debris-flow mathematical modelling M. Trujillo-Vela et al. https://doi.org/10.1016/j.earscirev.2022.104135
- Addressing current and future challenges in the engineering geology community - A young engineering geologist’s perspective E. Karantanellis et al. https://doi.org/10.1007/s10064-026-04847-w
- Sensitivity and Calibration of Three‐Dimensional SPH Formulations in Large‐Scale Landslide Modeling S. Li et al. https://doi.org/10.1029/2022JB024583
- On the estimation of landslide intensity, hazard and density via data-driven models M. Di Napoli et al. https://doi.org/10.1007/s11069-023-06153-0
- Mechanisms and river blocking effects of clustering debris flows on 20 August 2019 along the Minjiang River, Wenchuan, China Y. Luo et al. https://doi.org/10.1007/s12040-024-02502-0
- r.avaflow v4, a multi-purpose landslide simulation framework M. Mergili et al. https://doi.org/10.5194/gmd-18-9879-2025
- An integrated framework for wildfire emergency response and post-fire debris flow prediction: a case study from the wildfire event on 20 April 2021 in Mianning, Sichuan, China Y. Tang et al. https://doi.org/10.1007/s11069-025-07270-8
- Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al. https://doi.org/10.5194/nhess-24-823-2024
- The impact of terrain model source and resolution on snow avalanche modeling A. Miller et al. https://doi.org/10.5194/nhess-22-2673-2022
- Short to long term space-time prediction of rain-induced landslides under uncertainty A. Mondini et al. https://doi.org/10.1016/j.scitotenv.2025.179453
- Debris flow risk assessment via numerical simulation: a case study in Northeast China L. Wei et al. https://doi.org/10.1007/s10346-025-02545-4
- A Hybrid Theory-Driven and Data-Driven Modeling Method for Solving the Shallow Water Equations S. Yao et al. https://doi.org/10.3390/w15173140
- A deterministic two-phase model for an active suspension with non-spherical active particles using the Eulerian spatial averaging theory B. Deußen et al. https://doi.org/10.1063/5.0077735
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
Landslides, debris flows and other types of dense gravity-driven flows threaten livelihoods around the globe. Understanding the mechanics of these flows can be crucial for predicting their behaviour and reducing disaster risk. Numerical models assume that the solids and fluids of the flow are unstructured. The newly presented model captures the internal structure during movement. This important step can lead to more accurate predictions of landslide movement.
Landslides, debris flows and other types of dense gravity-driven flows threaten livelihoods...