Forecasting landslide sediment supply to streams using material point method
Bibliographic record
Abstract
In mountain drainage basins, channel morphology and river dynamics are heavily controlled by sediment budgets resulting from landslide activity (Schumm 1977, Church 1992, Montgomery and Buffington 1997). Depending on the type and volume of landslide sediments, river velocity, fluvial channel geometry, and the magnitude-frequency of hydro-meteorological events, downstream effects can be dangerous, particularly when the safety of lives and urban areas is threatened. A quantitative estimate of landslide sediment supplies and their influence on the morphology of fluvial systems are crucial information for predicting subsequent sediment transport dynamics, and, therefore, ensuring effective sediment management strategies.The objective of this study is to provide quantitative estimates of landslide sediment supply to the fluvial drainage network using the Material Point Method (MPM). MPM is a mesh-free physically-based numerical approach where the domain is discretized into material points that can move across a stationary Finite Element (FE) mesh (Sulsky et al. 1994, 1995, Abe et al. 2014, Yerro et al. 2019). The governing equations are solved at the nodes of the fixed computational grid for each new configuration of the material points. This feature makes the MPM more suitable than FE methods for studying large deformation phenomena, such as the propagation of landslide masses.The numerical method has been applied to study an earthflow event in the Northern Apennines (Italy) validated using multi-temporal DTM reconstructed from drone-based LiDAR surveys. Preliminary results are presented in terms of sediment budget quantification and variations in river cross-section. The comparison between predictions and observations provides valuable insights into the hillslope-channel coupling phenomenon and demonstrates the forecasting potential of the MPM. This preliminary study is a crucial step toward advancing sediment supply forecasting under changing climate scenarios. AKNOWLEDGEMENTSThis study has been carried out within the Project LASST “evaluating LAndslide Sediment Supply to sTreams and connectivity for sustainable, basin-wide sediment management” 20225S3Y7N_PE10_PRIN2022 - PNR M4.C2.1.1 – Funded by European Union – Next Generation EU - CUP: B53D23006810006 REFERENCESAbe, K., Soga, K., & Bandara, S. (2014). Material point method for coupled hydromechanical problems. Journal of Geotechnical and Geoenvironmental Engineering, 140(3), 04013033.Church, M. (1992). Channel morphology and typology. The river handbook, 1, 126-143.Montgomery, D.R., & Buffington, J.M. (1997). Channel-reach morphology in mountain drainage basins. Geological Society of America Bulletin, 109(5), 596-611.Schumm, S.A. (1977). The fluvial system. New York u.a: WileySulsky, D., Chen, Z., & Schreyer, H. L. (1994). A particle method for history-dependent materials. Computer methods in applied mechanics and engineering, 118(1-2), 179-196.Sulsky, D., Zhou, S. J., & Schreyer, H. L. (1995). Application of a particle-in-cell method to solid mechanics. Computer physics communications, 87(1-2), 236-252.Yerro, A., Soga, K., & Bray, J. (2019). Runout evaluation of Oso landslide with the material point method. Canadian Geotechnical Journal, 56(9), 1304-1317.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".