Feasibility and Storage Capacity of Water Harvesting Dams in Al-Ghadaf Valley / Western Iraq
Bibliographic record
Abstract
Finding alternatives to water sources has become an important and strategic issue.The impact of the quantities of water revenues due to climate change, as well as the establishment of multiple projects on the rivers that feed Iraq, becomes clear.This study focused on locating suitable sites for water harvesting dams in the valley of Al-Ghadaf, which is located in the western desert of Anbar Governorate.The entire area is 8772.5 km 2 , and water enters the valley of Al-Ghadaf through three sub-basins.The objective of this work is to carry out a hydrological examination and analyze data for the three basins utilizing the software HEC-HMS, which is among the most reliable and effective ways to determine the connection between precipitation and surface runoff.It was selected for several reasons that will be detailed later, to identify the optimal locations for water harvesting dams and their storage capacity.The maps were analyzed, and geographic data were to study the hydrology of the basins in valley Al-Ghadaf using ArcGIS Software Version 10.8 with HEC_HMS Version 4.11.Actual rainfall data were obtained from the Meteorology and Seismic Monitoring Authority for 2008-2022.Simulations using real rainfall data revealed that the greatest storage capacity inside Valley Al-Ghadaf was 114.72192M m 3 .The basins of valley Al-Ghadaf were worked on as water harvesting dams Maximum storage capacity of the three sub-basins were (65.32704M m 3 , 79.29792M m 3 , and 115.49952M m 3 ) respectively.The study shows that the harvested water in Valley Al-Ghadaf might be used with the lowest loss rate to keep aquatic ecosystems in equilibrium, collect and manage flood waters from valley basins, retain water storage for future seasons, and improve and sustain water quality.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".