Glacial isostatic adjustment modelling of the Coast Mountains of British Columbia
Bibliographic record
Abstract
The Coast Mountains in British Columbia contain over 10,000 km2 of glacial ice. While these glaciers have lost significant mass since the Little Ice Age (LIA; around 300 years before present), the melting rate has significantly increased over the past decade, likely due to the effects of climate change. The purpose of this study was to develop an approach to quantifying the isostatic response to LIA glacier change and investigate how it can further our understanding of the Earth’s rheology through GIA modelling. The Coast Mountains in southwestern British Columbia were chosen due to their significant ice mass loss since the LIA, their location in a tectonically active region, which includes a volcanic arc, and the presence of information of vertical land motion. The GIA models in this study use a wide range of Earth rheological parameters that are then constrained through comparison to observations of vertical land motion in the region. The study used available Global Navigation Satellite System (GNSS) vertical velocity data as the observable from seven GNSS sites in southwestern BC, using a combination of Western Canada Deformation Array (WCDA) and British Columbia Active Control System (BCACS) GNSS stations. Raw data were analyzed using the GIPSY 6.4 software following the Precise Point Positioning processing strategy. Two ice load histories were developed based on gridded estimates of present-day ice thicknesses in the region in order to simulate the change in the surface loading as the glacial ice mass fluctuates over time. Ice Load A used a simple uniform thickness change profile over 3 time-steps based on extrapolated modern melt rates. Ice Load B is more complex and utilized a published profile of glacier change through time basing the magnitude of volume changes on the volume-area scaling relationship with a range of coefficient values. This allowed for a range of ice change magnitudes to be tested. The Earth models used were spherically symmetric Preliminary Reference Earth Models (PREM). Their viscosity structure is based on VM5a for the transition zone and lower mantle, but with variable lithospheric thickness and asthenospheric viscosity. The goodness of fit for the modeled velocities were compared to the observed velocities using a normalized RMS (NRMS) statistic. Ice Load A models had a best fitting lithospheric thickness of 50 km and an asthenospheric viscosity of 2×1019 Pa s. For all variations of Ice Load B, the best fitting model parameters had lithospheric thicknesses ranging from 45 km to 55 km and asthenospheric viscosities between 6×1018 Pa s and 3×1019 Pa s. Corrected GNSS vertical velocity observations were tested to check the effects of interseismic vertical signal and assumed residual GIA from the Cordilleran Ice Sheet. However, the corrections did not improve the NRMS fit. Overall, the asthenospheric viscosity results from this study overlap with all the ranges found in the previous studies while lithospheric thicknesses agree with some past studies. The results of this study generally align with previous work and the current understanding of the Coast Mountains region and can inform a future round of sea-level projections for the region as ice mass loss continues in the Coast Mountains. This study serves to further refine constraints on Earth rheology and can be used to guide future work on GIA in the region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".