Stochastic Modeling of Extreme Dry Spells in Southeast Iran: Patterns, Trends, and Return Periods

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.