Modelling sediment dynamics at the basin scale : implications of changes in climate and hydrological regimes
Bibliographic record
Abstract
Basin-wide sediment dynamics are closely linked to hydrological processes and landscape and therefore expected to be susceptible to climate change. Simulating sediment transport through large basins presents a challenging problem to modellers; the relationship between water flux and sediment load is complex and non-linear, and significant sediment generation can occur over small spatial and time scales. To date, most studies have employed lumped empirical models that predict annual load at the outlet of a study basin, but do not consider variability across the basin or sub-annually. In this study, we adapt a physically-based, distributed suspended sediment transport model for large-scale use. The sediment model is integrated into the Terrestrial Hydrology Model with Biochemistry (THMB) as a routine to make use of THMB’s dynamic water routing. The coupled model is applied to the 230,000 km² Fraser River Basin (FRB) in British Columbia, Canada using 1) historical hydrological input to test the model and 2) synthetic input derived from Intergovernmental Panel on Climate Change (IPCC) scenarios A1B, A2, and B1 to study potential impacts of climate change. In both cases the input data is provided by the Pacific Climate Impacts Consortium (PCIC) and comes from simulations using the Variable Infiltration Capacity (VIC) model. Simulation results using historical inputs are compared with observations at five stations using the coefficient of determination (R²), Nash-Sutcliffe coefficient of efficiency (NSE), and percent bias (PBIAS) metrics. Overall, simulated load values match well with observed values, with the monthly simulations at the station nearest the outlet scoring R² = 0.78, NSE = 0.77, and PBIAS = -20%. Simulation results using climate scenario-driven inputs are studied for potential future changes in sediment dynamics. Results re- veal a general shift in hillslope erosion and sediment yield towards larger values from autumn to spring, reduced summer values, and an overall annual increase, with hillslope generation growing 35-45% from baseline levels and yield at the basin’s outlet increasing 10-15%. These physically-based results offer unique insights into the impacts of climate change on sediment processes within a large basin and their potential implications.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".