the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Jun 2026
Assessing Buried Landslide Rupture Surfaces Using Genetic Algorithms and Dynamic Flow Modeling
Abstract. Estimating the landslide volume and rupture geometry remains a critical challenge, particularly for landslides whose toe of the rupture surface is buried by displaced materials. This geometrical ambiguity leads to significant uncertainties in hazard assessment. To address this issue, this study proposes an integrated framework that couples a geometric search method with a physics-based dynamic model. This study employ the Genetic-Algorithm Ellipse-Referenced Idealized Curved Surface (GA-ER-ICS) to generate candidate rupture surfaces. Unlike traditional geometric fitting, the optimal rupture surface is constrained not only by topographic fit but also by the dynamic behavior of the post-failure motion. The validity of the approxi- mated geometry is verified by simulating the subsequent flow paths and deposition patterns using a GPU-accelerated two-phase grain-fluid model (MoSES_2PDF). The proposed method is validated against the 2009 Hsiaolin landslide and applied to the 2022 Provincial Highway No. 7 landslide event in Taiwan. Results demonstrate that the integrated approach successfully approximates the buried rupture surface, achieving a deposition coverage accuracy of over 75 % and reducing the uncertainty in volume estimation compared with the estimates derived from the difference between pre- and post-event Digital Elevation Models (DEMs). This study highlights the potential of using dynamic flow calibration to resolve static geometric indeterminacy in back-calculation of the landslide failure surface.
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RC1: 'Comment on egusphere-2026-1605', Anonymous Referee #1, 29 Jun 2026
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General Comments: This paper presents an innovative integrated framework that combines a Genetic Algorithm-based geometric search (GA-ER-ICS) with a two-phase dynamic flow model (MoSES_2PDF) to reconstruct buried landslide rupture surfaces. The computational efficiency achieved through GPU acceleration and the validation against historical events are highly commendable. The study addresses a significant challenge in post-disaster forensics. I recommend a minor revision to address the following points for clarity and better applicability of the research.ÂSpecific Comments:
- Consistency in Terminology Regarding Landslide and Flow The authors explicitly state that secondary mobilisation following the initial collapse, or rainfall-driven entrainment occurring after the main event are not represented. However, the manuscript intermittently uses terms such as flow and debris flow (e.g., in the name MoSES_2PDF). While definitions may vary among researchers, this study primarily targets the movement of a landslide mass modeled as a mixture of solid and fluid phases. Please ensure consistent terminology throughout the paper. It should be clarified that the motion is defined based on the proportions of the solid (soil/grain) and liquid (interstitial fluid) phases, and that the model treats the relatively smooth movement of the collapsed mass through a physics-based dynamic approach.
- Rationale for Parameter Settings (Basal Friction Angle delta_b) In Table 2, the basal friction angle delta_b for the Hsiaolin event is set to 16 degrees. While this value may be drawn from previous calibrations, it is exceptionally small. Please provide a more detailed explanation and justification for this specific value within the text, rather than relying solely on citations. Including information on the local geology, such as the degree of weathering and lithological characteristics (e.g., weathered sedimentary rock), would be highly beneficial. This will help future researchers determine whether this value is applicable to their study areas or if site-specific re-calibration is required.
Comment on Future Outlook: The authors mention coupling the framework with rainfall-driven hydrological forcing as an avenue for future work. In regions where borehole observations (e.g., groundwater level monitoring or changes in water paths) are available, such data could potentially enhance the estimation of pore-pressure-driven stability changes. I would be interested to hear the authors' perspectives on how such subsurface monitoring data might be integrated into their proposed framework in the future.ReplyCitation: https://doi.org/10.5194/egusphere-2026-1605-RC1 -
CC1: 'Comment on egusphere-2026-1605', Luca Sarno, 31 Jul 2026
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In order to estimate the landslide volume and rupture geometry, this work proposes an interesting integrated approach that couples a geometric search method, based on a genetic algorithm (GA-ER-ICS), with the two-phase depth-averaged model MoSES_2PDF. Two case studies located in Taiwan, namely the 2009 Hsiaolin landslide and the 2022 Provincial Highway No. 7 landslide, are reported for method validation and testing. The results are interesting and the proposed method is found to achieve a high deposition coverage accuracy.
I believe that the method can be effectively used to improve the estimation accuracy of the landslide rupture geometry, especially compared to classical DoD methods when the toe of the landslide scarp is not clearly visible due to subsequent deposition processes.
Since the method crucially leverages the high computational efficiency of GPU-accelerated numerical simulations, it would be useful to provide some additional details about the computation times for different numerical simulations and mention the GPU card and other hardware specs of the PC employed for simulations.
Additionally, to better assess the robustness of the method, it would be useful to briefly discuss about the sensitivity of the various MoSES_2PDF model parameters listed in Tab. 2. I expect that the most sensitive parameter is the angle of basal friction. For example if the angle of basal friction was varied by +-3 degrees, would the numerical results be noticeably different in terms of run-out distances and deposition coverage accuracy? I think that a brief analysis on these sources of uncertainty could enhance the impact of the work.
Citation: https://doi.org/10.5194/egusphere-2026-1605-CC1
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