The Impact of Climate and Land Cover Change on the Cryosphere and Hydrology of the Mackenzie River Basin, Canada
Bibliographic record
Abstract
Abstract Northern basins are projected to continue warming at rates higher than the global average, with the impacts of warming compounded by concomitant deglaciation, permafrost thaw and vegetation shifts. The Mackenzie River Basin drains headwaters in the glaciated Canadian Rockies to the Arctic Ocean and is mostly underlain by permafrost. Scenarios of future change in this basin were simulated using the MESH distributed hydrological‐cryospheric land surface model. MESH was forced with bias‐corrected, downscaled RCM forcings and parameterized with a deep subsurface profile, organic soils, and glaciers. The model, validated against discharge, snowpack, and permafrost observations, was used to simulate 21st century hydrology and permafrost dynamics under the RCP8.5 emissions scenario, incorporating projected land cover change applied at two discrete time steps (2021 and 2065). The findings indicate a rapid acceleration of permafrost thaw. By the 2080s, most of the basin will be devoid of permafrost. By late century, river discharges shift to earlier and higher peaks in response to projected increases in precipitation, temperature and snowmelt, despite increases in evapotranspiration from longer snow‐free seasons. Baseflow discharges increase in winter, due to higher precipitation and increased basin connectivity from permafrost thaw resulting in enhanced groundwater flow. Subsurface moisture storage rises slightly but the liquid water fraction increases dramatically, increasing subsurface runoff and river discharge. Canadian Rockies' deglaciation reduces summer and annual discharge in the Athabasca and Peace headwaters. Downstream and northward of the mountain headwaters the direct impacts of climate change on river discharge dominate those of changing land cover and glaciers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".