Relating regional acceleration events to hydroclimatic inputs for slow-moving deep-seated landslides in Western Canada
Bibliographic record
Abstract
Deep-seated landslides in colluvium derived from glacial sediments and shales blanket river valley slopes in the Western Canada Sedimentary Basin (WCSB) and are traversed by linear infrastructure and urban development. Porter et al (2019) estimated that the infrastructure maintenance and damage costs are in the order of $ 400 million (CDN) annually. In the spring of 2020, widespread accelerations of landslides in the northern portions of the WCSB led to the initiation of a multi-year study to better understand the relationships between short and longer-term hydroclimatic trends in relation to historical landslide activity. Data from over 550 subsurface monitoring points (slope inclinometers and shape accelerometer arrays) were collected for over 100 slopes between the early 1980’s to present. A multi-stage cleaning process was necessary to minimize errors (installation, human, sensor) so that readings represent measurements of deep-seated landslide movement and reliably constrain discrete acceleration events. The concept of a “landslide year” was developed to delineate the annual movement cycle for landslides in the region and was defined as the period that starts in the spring when snowmelt infiltrates into the ground and finishes which the ground freezes in the autumn. Only displacement values that reliably constrained the landslide year were maintained in the database and, for sites with at least three years of readings, these values at each monitoring location were normalized against all of the readings for that site. This allowed for a more consistent comparison of the magnitude of displacements across sites and the region.In parallel, historical hydroclimatic variables obtained from the ECWMF ERA5-Land reanalysis dataset (Muñoz-Sabater et al., 2021) were accessed, analyzed and reviewed. As with the displacement data, different approaches were assessed to provide normalized values that could represent “extreme” events and trends in the hydroclimate that could be compared across the region. The variables assessed focused on the antecedent soil moisture and the total water introduced during the landslide year from both snow melt and precipitation. These values, both absolute and normalized, allowed for both spatial and temporal analyses and data visualizations.Random forest models were used to establish the relative importance of different hydroclimatic inputs in predicting normalized annual landslide displacements. The hydroclimatic variables seen as the most important and most useful for application in an early warning system were then evaluated in terms of their site-level “predictive power” when compared against the normalized displacement data. The test variables utilized were normalized Layer 4 soil moisture at the start of the landslide year, normalized Layer 4 soil moisture trend at the start of the landslide year and maximum normalized 60-day total water inputs within the landslide year. These tests yielded positive results in terms of correlation between combinations of the chosen hydroclimatic inputs and landslide displacement trends. Further development and testing of hydroclimate thresholds as a basis for a regional landslide awareness and early warning system is in progress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".