Incorporating a GIS-Based Approach and SWAT Model to Estimate Sediment in the Western Desert of Iraq
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.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".