Prediction of meteorological and hydrological phenomena by different climatic scenarios in the Karkheh watershed (south west of Iran)
Bibliographic record
Abstract
This research evaluates effects of climatic change on future temperature, precipitation and flow discharge in the Karkheh watershed (a watershed in south west of Iran). For this purpose, it utilizes general circulation models (GCMs) and the non parametric Mann-Kendall (MK) trend test. Considered hydrometric station is the Jelogir station at the upstream of the Karkheh dam. Base time period is 1971-2014 and future time period is 2030- 2073 for prediction of meteorological and hydrometric phenomena in the Jelogir station. For GCM model, the Canadian Climate Change Scenarios Network (CCCSN) database represents data of HadCM3 model for A2 and B2 scenarios. For using in a watershed, this research applies SDSM downscaling model and introduces predicted precipitation and temperature of future time period to IHACRES model for prediction of flow discharge. Also the non parametric Mann-Kendall trend test and the Theil–Sen approach (TSA) estimator distinguishes trend of observed and predicted data. Results of scenarios A2 and B2 have not much difference. Different climatic scenarios show that temperature increases and precipitation and flow discharge decrease, also MK test and TSA estimator represent that slope of their variations will slow down in future and most of changes are related to winter and spring.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".