Incorporating a GIS-Based Approach and SWAT Model to Estimate Sediment in the Western Desert of Iraq
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Bibliographic record
Abstract
Sedimentation significantly impedes dam efficiency by diminishing storage capacity, necessitating planning and maintenance strategies that accurately identify key sediment sources in watersheds and restore sediment productivity.This study applied a physicalbased SWAT (Soil and Water Assessment Tool) watershed model to quantify the sedimentation of the H-3 Houran Dam in the Houran valley, Western Iraq.The SWAT model was deployed for the period from 1/1/2004 to 31/12/2021, estimating the daily and annual sediment and surface runoff from the Houran valley.The model's performance was assessed using an error ratio criterion between the actual field-measured sediment and the simulated sediment yield; the results demonstrated a favourable error rate of less than 1%.Sediment spatial distribution varied across the lake, with a higher per-unit-area sediment concentration near the dam body, despite a lesser total quantity within the dam basin.This discrepancy was attributed to increased downstream runoff and other basin characteristics such as slope and rock type.Our findings corroborate the appropriateness of this methodology for water resource management, particularly in areas with limited data.Contrary to technical reports suggesting an annual sediment transport of 60 tons per square kilometre in Western Iraq, this study found a more plausible figure of 37.8 tons.For the period 2004-2021, the actual sediment collected in the dam basin was calculated to be 700,278 tons, with a daily runoff over 18 years of 12.2 m 3 /s.The simulated sediment yield was 37,470.9tons, which calibrated to 707,516.4 tons, maintaining an error rate of 1% for the parameters SPCON, SPEXP, CN2.This study thus provides valuable insights into sediment management for dam efficiency.
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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 | 0.000 |
| 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 it