Discharge Capacity of Euphrates River from Haditha Dam to Ramadi Barrage
Bibliographic record
Abstract
Deferent studies were conducted on the Euphrates River focusing on water quality models, computing Manning's coefficient bed sediment load, comparing cross-sections of the river with different periods, and determining the capacity for the Reaches with some numerical models.There is a lack of studies to specify the capacity of the Euphrates River under normal operating conditions and during floods in the current conditions of the river's cross-section.This research was carried out to study the capacity flowrate of the Euphrates River from Haditha Dam to Ramadi Barrage for the length of 160 km.The study conducted a numerical model with HEC-RAS 6.1 to simulate the capacity of the study reach.The geometry of the river was represented by more than a hundred crosssections.The model was calibrated using some observed discharges at the Heet gage station for records of the last five years.The calibration result appeared that the minimum Root Mean Square Error of 0.267 can be measured when the roughness coefficient of 0.026, 0.024 for the Haditha Dam to Heet and Heet to Ramadi Barrage respectively, and the flood plain records manning (n) at the value of 0.03 for all the study reach.The study reach includes five inflow valleys feeding Euphrates River in rainy seasons and has one lateral outflow called Al-Warrar which is located at about 3 km upstream of Ramadi Barrage.The model result indicated Euphrates River within the study reach can pass 3000 m3/s with and without lateral inflow, and the water level in the two cases is still more than 1m from river levees.On the other hand, the predicted discharge of 4000 m 3 /s for a return period of 500 years cannot pass safely through the Euphrates River without modifying the choking cross-sections, especially at the last 30 km of the study reach.The required modification was conducted in the model and the river passed the predicted discharge with a freeboard of 1m.
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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 | 0.000 |
| 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) | 0.001 | 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".