Effect of Climate on Temporal Distribution Pattern of Rainfall and Comparing With Each other and Known Patterns Case Study: Ardebil Province - Iran
Bibliographic record
Abstract
Rainfall distribution pattern is one of the most important factors in simulation of runoff hydrograph and hydraulic structure design. The pattern is often different in various climates. The present study was formulated in order to determine temporal rainfall distribution pattern and comparing it with SCS, WMS, and Huff patterns in four regions (namely Haruchay, Gharasou, Meshkinchay, and Darrehroud) in Ardebil Province – Iran. For this aim, temporal rainfall distribution pattern was assigned through Huff and Pilgrim Method by use of the data related to seven rain gauge stations within the mentioned regions. Afterwards, predominant climate of each region was evaluated and compared via Do Martin Method. By using MAE, RE, RMSE, ME, and W statistics, the patterns for each region were compared with those of SCS, WMS, and Huff. The results obtained from the present study indicated that rainfall distribution patterns in different regions of Ardebil Province are different. In semi-humid climate in southern regions (i.e. Haruchay), semiarid regions (i.e. Gharasou and Meshkinchay), and arid regions (Darrehroud),the maximum rainfall rates were in the first quarter (onset of rainfall), in the third quarter (midst of rainfall), and in the fourth quarter (end of rainfall), respectively. The results acquired from comparison of the patterns obtained for the regions with those mentioned above demonstrated that relative to the regions’ patterns, Huff-1st and Huff-4th patterns experienced the lowest and highest error rates, respectively. On the contrary, with regard to all statistical results, application of WMO, SCS, and Huff-3rd patterns provide more realistic estimations in Herochay and Meshkinchay, Darrehroud, and Gharasou, respectively.
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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 | 0.000 | 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) | 0.001 | 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".