Spatially distributed erosion and sediment yield modeling in the upper Indus River basin
Bibliographic record
Abstract
Spatially distributed erosion rates and sediment yields are predicted in the mountainous upper Indus River basin with coupled models of erosion and sediment delivery. Potential erosion rates are calculated with the Thornes model in combination with a surface runoff model. Sediment delivery ratios (SDRs) are hypothesized to be a function of travel time of surface runoff from catchment cells to the nearest downstream channel. Modeled monthly erosion rates for the upper Indus River basin indicate that 87% of the annual gross erosion takes place in the three summer months. The erosion risk map suggests that the areas with the greatest erosion potential are concentrated in subbasins with high relief and a substantial proportion of glacierized area. Lower erosion rates can be explained by the arid climate and low relief on the Tibetan Plateau and by the dense vegetation and lower relief in the lower monsoon subregion. High erosion rates (>1.0 mm a −1 ) occur over 66.4% of the basin area. The model predicts an average annual erosion rate of 3.2 mm a −1 or 868 Mt a −1 , which is approximately 4.5 times the long‐term observed annual sediment yield of the basin. The predicted annual basin sediment yield is 244 Mt a −1 , which compares reasonably well to the measured value of 195.1 Mt a −1 . The overall sediment delivery ratio in the basin is calculated as 0.28. Model results indicate that higher delivery ratios (SDR > 0.6) are found in 18% of the basin area, mostly located in the high‐relief subbasins. The sediment delivery ratio is lower than 0.2 in 70% of the basin area. The Indus subbasins generally show an increase of sediment delivery ratio with basin area. Model evaluation based on accuracy statistics suggest “very good” to “satisfactory” performance ratings for predicted sediment yields. The presented modeling framework requires relatively few data, all of which can be derived from global data sets. It therefore can be used to predict erosion and sediment yield in other ungaged or poorly gaged drainage basins.
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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 | 0.001 | 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 | 0.001 |
| 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".