A network model for prediction and diagnosis of sediment dynamics at the watershed scale
Bibliographic record
Abstract
We present a semi‐distributed model that simulates suspended sediment export from a watershed in two stages: (1) delivery of sediments from hillslope and bank erosion into the river channel, and (2) propagation of the channel sediments through the river network toward the watershed outlet. The model conceptualizes a watershed as the collection of reaches, or representative elementary watersheds (REW), that are connected to each other through the river network, and each REW comprises a lumped representation of a hillslope and channel component. The flow of water along the stream network is modeled through coupled mass and momentum balance equations applied in all REWs and sediment transport within each REW is simulated through the sediment balance equations. Every reach receives sediment inputs from upstream REWs (if present) and from the erosion of adjacent hillslopes, banks and channel bed. We tested this model using 12 years (1982–1993) of high temporal resolution data from Goodwin Creek, a 21.3 km2 watershed in Mississippi, USA. The model yields good estimates of sediment export patterns at the watershed outlet, with Pearson correlation coefficient (R value) of 0.85, 0.87, and 0.95 at daily, monthly, and annual resolution, respectively. Furthermore, the model shows that the dynamics of sediment transport are controlled to a large extent by the differences in the behavior of coarse and fine sediment particles, temporary channel storage, and the spatial variability in climatic forcing. These processes have a bearing on the patterns of sediment delivery with increasing scale.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".