Evaluation of the Minimum Instream Flow: A Case Study of Shatt-Al Hillah River in Babylon Governorate
Bibliographic record
Abstract
Shatt Al-Hillah is Iraq's largest and most important Euphrates River irrigation system. From Al-Hindiyah Barrage to Al-Dagharah Regulator, it rises 100 kilometers left of the Euphrates River. It is controlled by Shatt Al-Hillah Head Regulator, which has a design discharge of 350 m 3 /s. This river is used for agricultural, industrial, and municipal reasons, and it is considered a tourist attraction. To meet agricultural, municipal, and industrial needs, water amounts must be verified regularly. Climate change and a lack of upstream revenues have reduced water levels in Shatt Al-Hillah. This study proposes water distribution solutions by computing the minimum instream flow (MIF) to ensure irrigation project water shares in Shatt Al-Hillah Basin and the water level upstream of the Al-Dagharah Regulator to meet water requirements in Shatt Al-Diwaniyah basin. The hydraulic model required field measurements. It contained Shatt Al-Hillah River crosssections, drainages, and water levels. River Analysis System HEC-RAS 5.0.7 software simulated flow in the research area using a one-dimensional hydraulic model. Three scenarios have been provided to compute the lowest instream flow discharged from the downstream head regulator (Shatt Al-Hillah Regulator) following model calibration and verification. The results showed that all intakes of irrigation networks along the river in the research region can release 99 m 3 /s with a consumption discharge of 48.67 m 3 /s utilizing a 3-day irrigation interval. For all intakes with a consumption discharge of 32.5 m 3 /s, an irrigation interval of two days per week can release 83 m 3 /s. According to the database of discharge releases from Shatt Al-Hillah Regulator for the last three years, the river flow rate at the head regulator is 90 m 3 /s, so the third scenario with 83 m 3 /s and a two-day per week irrigation service interval is recommended.
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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".