Rainfall Prediction and Runoff Modelling under Climate Change Scenarios for Tigris River from Mosul to Baghdad Cities
Bibliographic record
Abstract
The study aims to examine the impacts of future climate change on the rainfall quantiles for the main sites of the Tigris River basin, including Mosul, Tikrit, Sammara, and Baghdad.There are two approaches, first: the global climate models (GCMs): (CMCC-CM2-SR5, CNRM/ESM2-1, EC-Earth3/Veg, MPI-ESM1/2-LR, MRI-ESM2/0, and NorESM2/MM) have been developed to show the future climate change depending on the precipitation data.They were designed along three periods, the reference period , the near-term (2020-2040), and the mid-term (2041-2060).Second, the prediction of the expected Rainfall-Depth (Q) for a return period (2-100 years) based on the rainfall data for 2000-2021 by applying the probability distribution function using the HEC-SSP software.Then, determined the expected Runoff-Depth depending on the results of the predicted probability of Rainfall-Depth for a specific return period and applied the Soil Conservation Service-Curve Number (SCS-CN) model to estimate the CN values of the catchment area from the soil classification according to maps of Land use/cover (LULC) and hydrologic soil grouping (HSGs) generated by the ArcGIS software from the FAO Soils Portal website using Harmonized World Soil Database (HWSD) and the HEC-HMS.The HEC-RAS program is used to create flood maps by simulating runoff depth for forecasting 100 years.The results of the (GCMs) models found the ensemble precipitation change tends to increase at Mosul and Tikrit sites, while Sammara and Baghdad sites noticed no significant changes for both the near and midterm.The flood map reveals that the expected runoff has covered the urban areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".