A Sequentially Coupled Catchment‐Scale Numerical Model for Snowmelt‐Induced Soil Slope Instabilities
Bibliographic record
Abstract
Abstract The frequency of snowmelt‐induced soil slope instabilities is increasing in some seasonally cold regions because of climate change. Reliable hazard assessment and risk mitigation of snowmelt‐induced landslides require physically‐based prediction models. However, existing models either apply only at the slope scale or assume precipitation as the sole landslide trigger. In doing so, they neglect the complexity and coupled nature of the thermo‐hydro‐mechanical processes leading to slope instability in seasonally cold regions (such as snow accumulation and melting, infiltration and surface runoff, soil saturation, pore water pressure buildup and dissipation). Here, we present a spatially distributed and sequentially coupled numerical model to simulate snowmelt‐induced slope instabilities at the catchment scale. The model accounts for temperature‐dependent changes in the soil hydraulic behavior related to changes in water state by means of a routine implemented in a geographic information system. We verified the performance of the model using a case study of spring snowmelt‐induced soil slope failures that occurred after the 2004 Mid‐Niigata earthquake in Japan. Considering limitations and simplifications, the model was able to predict the triggering condition, magnitude, and spatial distribution of the snowmelt‐induced landslides with a satisfactory degree of accuracy. We believe that the robustness and simplicity of our numerical approach make it suitable for implementation in early warning systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".