A decadal‐scale numerical model for wandering, cobble‐bedded rivers subject to disturbance
Bibliographic record
Abstract
Abstract Alluvial rivers are composed of self‐formed channels which are sensitive to disturbances in their flow and sediment‐supply regimes. Regime changes commonly occur over decadal and longer timescales and can be caused by anthropogenic alterations such as dam construction and removal. Advances in numerical modeling have increased our ability to explore geomorphic adjustments over long timescales; however, many models designed to be run for decades or longer assume that banks are immovable or that channel width is constant. Since river channels often respond to disturbance by adjusting their geometry, this is a significant shortcoming. To investigate the impact of long‐term sediment supply alterations on channel geometry and stability, we have adapted MAST‐1D, a reach‐scale bed evolution model, to incorporate functions for bank erosion, vegetation encroachment, and local avulsions. The model is designed for medium‐large, coarse multithreaded rivers and can be run over long (decades–centuries) timescales. Bank erosion is a function of the mobility and transport capacity for structurally‐important grains which protect the bank toe. Vegetation growth is proportional to point bar width and occurs during conditions of low shear stress. Local avulsions occur when aggradation causes channel depth to drop below a threshold. We apply the model to the Elwha River in Washington, USA with the goal of investigating if and when the river recovers from dam emplacement and removal. The Elwha was dammed for nearly 100 years, and then two dams were removed, releasing a large pulse of sediment. We have modeled the set of reaches between the two dams. Our simulations suggest that channel response to dam emplacement occurs gradually over several decades but that the channel recovers to near pre‐dam conditions within about a decade following the removal. The dams leave a lasting legacy on the floodplain, which does not completely recover, even after two centuries. © 2019 John Wiley & Sons, Ltd.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".