Rainfall Trends and Impact on Water Resources: Case of Southwestern Saudi Arabia
Bibliographic record
Abstract
The various annual reports of the Intergovernmental Group of Climate Experts (IPCC) affirm that the earth has been experiencing climate change for nearly half a century, resulting in a thermal increase and a decrease in rainfall. This substantiated in several studies. In Saudi Arabia, where the climate is characterised by low rainfall and high temperatures, it has attracted the attention of several studies. Some have looked at thermal rise while others have looked at rainfall trends. In this study we seek to take stock of the evolution of rainfall quantities at different time scales in the southwest of this country during the period 1985–2020. To do this, we use the rainfall data of seven stations located in a space that is distinguished by a physical duality, and hence a rainfall duality. Indeed, contrasting a relatively rainy west opposed to an eastern area where the rainfall is low. Does this situation create a duality of water resources? Statistical processing and graphic translation are an effective way of accurately capturing and quantifying this evolution. This will allow a focus on the impact on water resources. Such resources are a necessary guarantee for any sustainable development.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".