Construction of sediment budgets in large scale drainage basins : the case of the upper Indus River
Bibliographic record
Abstract
High rates of soil loss and high sediment loads in rivers necessitate efficient monitoring and quantification methodologies so that effective land management strategies can be designed. Constructing a sediment budget is a useful approach to address these issues. Quantifying a sediment budget using classical field-based techniques, however, is labour intensive and expensive for poorly gauged, large drainage basins. The availability of global environmental datasets in combination with GIS techniques provides an opportunity for studying large basins. Following this approach, a framework is presented for constructing sediment budgets for large, data-sparse drainage basins, which is applied to the mountainous upper Indus River basin in northern Pakistan. The methodological framework consists of five steps: (1) analyzing hydro-climatological data for dividing the drainage basin into characteristic regions, and calculating sediment yields; (2) investigation of major controls on sediment yields; (3) identification and mapping of sediment source areas by spatially distributed modelling of erosional processes; (4) spatially distributed modelling of sediment yields; and (5) carrying out the sediment budget balance calculation at the basin outlet. Further analysis carried out on the Indus data has enabled a better understanding of sediment dynamics in the basin. Analysis of the available hydro-climatological data indicates that the basin can be subdivided into three characteristic regions based on whether runoff production and subsequent sediment generation is controlled by temperature (Region 1, upper, glacierized sub-basins), precipitation caused by the monsoon and western disturbances (Region 3, lower sub-basins), or a combination of the two (Region 2, middle reach sub-basins). It is also demonstrated that contrary to the conventional model, the specific sediment yield increases markedly with drainage area along the Indus River. An investigation of major controls on specific sediment yield in the basin indicates that percent snow/ice cover is a major land cover control for specific sediment yield. Spatially distributed erosion modelling predictions indicate that 87% of the annual gross erosion takes place in the three summer months with greatest erosion potential concentrated in sub-basins 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 sub-region. The model predicts an average annual erosion rate of 3.2 mm/a or 868 Mt/a. Spatially distributed sediment yield predictions made with coupled models of erosion and sediment delivery indicate that the Indus sub-basins generally show an increase of sediment delivery ratio with basin area. The predicted annual basin sediment yield is 244 Mt/a and the overall sediment delivery ratio in the basin is calculated as 0.28. The long-term mean annual sediment budget, based on mass balance, is characterized by a gross erosion of 762.9, 96.7 and 8.4 Mt, and a gross storage of 551.4, 66.1, and 6.5 Mt in the upper, middle, and lower regions of the basin, respectively. The sediment budget indicates that the major sources of eroded sediment are located in the Karakoram, in particular in the Hunza basin. Substantial sediment storage occurs on the relatively flat Tibetan Plateau and the Indus River valley reach between Partab Bridge and Shatial. The presented framework for sediment budget construction requires relatively few data, mostly derived from global datasets. It therefore can be utilized for other ungauged or poorly gauged drainage basins of the world.
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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) | 0.000 | 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 | 0.000 | 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 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".