Stochastic Modeling of Extreme Dry Spells in Southeast Iran: Patterns, Trends, and Return Periods
Bibliographic record
Abstract
Southeast Iran, an arid and highly vulnerable region, faces significant challenges from recurrent droughts. Understanding the behavior of extreme dry spells is critical for effective water resource management and drought risk assessment. This study provides a comprehensive stochastic analysis of the Longest Annual Dry Spell (LADS) across this region. Daily precipitation data was utilized from eight meteorological stations spanning 38 years (1985–2022). LADS series were extracted using a precipitation threshold of ≥ 1 mm/day. Long-term trends in LADS were assessed using Sen’s slope estimator and the Mann–Kendall test for statistical significance. To model the probability of extreme events, five probability distributions—Generalized Extreme Value (GEV), Generalized Pareto (GP), Pearson Type III (PE3), three-parameter log-normal (Lnorm3), and generalized logistic (Glogis)—were fitted to the LADS data, with parameters estimated via the robust L‑moments method. The best-fit distribution was identified using Kolmogorov–Smirnov, Anderson–Darling, and Chi-squared goodness-of-fit tests. Subsequently, LADS return periods (2, 5, 10, 25, 50, and 100 years) were calculated. Results indicate a generally non-significant increasing trend in LADS duration across the study area. The PE3 distribution emerged as the most suitable model for LADS frequency analysis in Southeast Iran. Notably, spatial variations exist: Zabol station (north) exhibits the highest LADS risk for shorter return periods (e.g., 185 days for T = 10 years), whereas Iranshahr station (center) shows the highest risk for longer return periods (e.g., 372 days for T = 100 years). These findings offer crucial quantitative insights into extreme dry spell hazards, supporting targeted drought preparedness and adaptation strategies in this climate-sensitive region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".