Regional frequency analysis and spatial pattern characterization of Dry Spells in Iran
Bibliographic record
Abstract
ABSTRACT This paper presents a methodology for regional frequency analysis and spatial pattern features of the Annual Maximum Dry Spell Length (AMDSL) as an indicator of drought conditions using the well‐known L‐moments approach and statistical‐based methods. Applying Ward's cluster‐analysis method identifies eight regions with distinctive AMDSL behaviours for Iran. Homogeneity testing indicates that most of these regions are homogenous. The goodness‐of‐fit test ZDist shows that Generalized Logistic; Generalized Extreme Value and Pearson type III, distributions fit best for most regions. The spatial pattern of L‐Moment statistics demonstrates that although the northwestern and northern parts of the country experience short dry spells, these periods are inconstant, and extreme dry spell events may happen in these areas. Almost all spatial mapping of AMDSLs at different probabilistic levels demonstrates that dry spells increase gradually from west to east and from north to south, and the southern parts (especially along the Persian Gulf and Oman Sea) and central areas, including most agricultural lands, stand out as the most sensitive to soil moisture deficits, because of longer lasting droughts. © 2013 Royal Meteorological Society
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".