Fluvial Response to Climate Change in the Pacific Northwest: Skeena River Discharge and Sediment Yield
Bibliographic record
Abstract
Changes in climate affect the hydrological regime of rivers worldwide and differ with geographic location and basin characteristics. Such changes within a basin are captured in the flux of water and sediment at river mouths, which can impact coastal productivity and development. Here, we model discharge and sediment yield of the Skeena River, a significant river in British Columbia, Canada. We use HydroTrend 3.0, two global climate models (GCMs), and two representative concentration pathways (RCPs) to model changes in fluvial fluxes related to climate change until the end of the century. Contributions of sediment to the river from glaciers decreases throughout the century, while basin-wide overland and instream contributions driven by precipitation increase. Bedload, though increased compared to the period (1981–2010), is on a decreasing trajectory by the end of the century. For overall yield, the model simulations suggest conflicting results, with those GCMs that predict higher increases in precipitation and temperature predicting an increase in total (suspended and bedload) sediment yield by up to 10% in some scenarios, and those predicting more moderate increases predicting a decrease in yield by as much as 20%. The model results highlight the complexity of sediment conveyance in rivers within British Columbia and present the first comprehensive investigation into the sediment fluxes of this understudied river system.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".