An evaluation and comparison of hydroclimatic data preceding extremely rapid glaciolacustrine landslides
Bibliographic record
Abstract
Deep-seated landslides in overconsolidated glaciolacustrine materials typically cycle through episodic periods of gradual acceleration and deceleration. The 1973 Attachie landslide (BC, Canada) and 2014 Oso landslide (Washington, USA) are well-known examples of landslides that deviate from this trend, instead failing extremely rapidly, with considerable runout that dammed the Peace River (Attachie) and impacted a community nearly 1.5 km away, resulting in 43 fatalities (Oso). Given the velocity and runout distance of these two landslides, further characterization of the landslide, priming, and trigger mechanisms may help manage geohazard risk for other landslides in similar terrain.The landslide mechanisms and antecedent climatic conditions prior to failure have been relatively well studied for the Attachie and Oso landslides. As part of these studies, hydroclimatic re-analysis tools have been applied, correlating soil moisture data with precipitation records to understand the dominant timescale by which hydroclimatic conditions may have triggered activity within these landslides in the past.In spring 2022, another extremely rapid landslide derived from glaciolacustrine materials occurred on the Halfway River, less than 10 km away from and initiating within the same geological unit as the 1973 Attachie landslide. The objectives of this study are twofold: to apply the same hydroclimatic re-analysis and precipitation review methodology to the Halfway River landslide, and to compare hydroclimatic trends across all three landslides. Comparison of landslide morphology, mechanisms, and material properties between these landslides are left to future research.Soil moisture and precipitation data were obtained from the land component of the ERA5 climatological re-analysis data produced by Copernicus Climate Change Service of the European Union. At the Halfway River slide, soil moisture (1-3 m depth) was above the monthly average for 65% of the months since over the 8-year period prior to the failure, with above-average annual soil moisture in 5 of the 8 years. Soil moisture and precipitation at the time of failure were not exceptional, although the failure occurred during the first rain-on-snow event in above-zero °C conditions of the year, which may be the triggering event. Annual precipitation and soil moisture in the year prior to the April 2022 failure were below average, indicating that one year of drier-than-average conditions may be insufficient in arresting the deformation processes that are hypothesized to predicate these extremely rapid failures.No discrete trigger was identified for the Attachie landslide. The dominant theory is that a longer-term internal deformation and acceleration trend associated with a 10-to-15-year period of above-average soil moisture preceding the 1973 failure caused the event. At the Oso landslide, a possible triggering event was identified from a nearly one in 10-year soil moisture peak, resulting from both a longer-term elevated soil moisture trend and three weeks of intense rainfall. This occurred in the context of a 4-year period of above-average precipitation. While it is likely that a variety of processes contributed to the extremely rapid failures of these landslides, these examples support the current hypothesis that multi-year moisture trends drive gradual deformation, preconditioning these slopes for extremely rapid failures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".