Development and Application of a Large‐Scale, Physically Based, Distributed Suspended Sediment Transport Model on the Fraser River Basin, British Columbia, Canada
Bibliographic record
Abstract
Modeling sediment transport through large basins presents a challenging problem. The relation between water flux and sediment load is complex, and substantial erosion and transport can occur over small spatial and temporal scales. Analysis of large‐scale basins often relies on lumped empirical models that do not consider spatial or subannual variability. In this study, we adapt a small‐scale, mechanistic, distributed suspended sediment transport model for application to large basins. The model is integrated into the Terrestrial Hydrology Model with Biochemistry to make use of the Terrestrial Hydrology Model with Biochemistry's dynamic water routing. The coupled model is applied to the 230,000‐km2 Fraser River Basin in British Columbia, Canada, using climatic and hydrological inputs provided by a historical run of the Variable Infiltration Capacity model. Hourly simulations are aggregated into monthly and long‐term averages which are compared against observations. Simulated long‐term lake sedimentation values are within an order of magnitude of observations, and monthly load simulations have an average R2 of 0.70 across the five study stations with available data. Model results indicate that sediment loads from tributaries do not heavily influence dynamics along the main stem and suggest the importance of network connectivity. Sensitivity analysis indicates that models may benefit from characterizing bed load irrespective of its contribution to total sediment load. Historical simulations over the 1965–2004 period reveal important changes in sediment dynamics that could not be captured with a lumped model, including a decrease in basin sediment load interannual variability driven by changes in runoff and load variability within a key subbasin.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".